{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第五课 CNN图像分类\n",
    "\n",
    "褚则伟 zeweichu@gmail.com\n",
    "\n",
    "参考资料\n",
    "- [Stanford CS231n](http://cs231n.github.io/convolutional-networks/)\n",
    "- [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)\n",
    "- [VGG](https://arxiv.org/pdf/1409.1556.pdf)\n",
    "- [ResNet](https://arxiv.org/pdf/1512.03385.pdf)\n",
    "- [DenseNet](https://arxiv.org/pdf/1608.06993.pdf)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch Version:  1.0.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "print(\"PyTorch Version: \",torch.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先我们定义一个基于ConvNet的简单神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 20, 5, 1) # 28 * 28 -> (28+1-5) 24 * 24\n",
    "        self.conv2 = nn.Conv2d(20, 50, 5, 1) # 20 * 20\n",
    "        self.fc1 = nn.Linear(4*4*50, 500)\n",
    "        self.fc2 = nn.Linear(500, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # x: 1 * 28 * 28\n",
    "        x = F.relu(self.conv1(x)) # 20 * 24 * 24\n",
    "        x = F.max_pool2d(x,2,2) # 12 * 12\n",
    "        x = F.relu(self.conv2(x)) # 8 * 8\n",
    "        x = F.max_pool2d(x,2,2) # 4 *4 \n",
    "        x = x.view(-1, 4*4*50) # reshape (5 * 2 * 10), view(5, 20) -> (5 * 20)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x= self.fc2(x)\n",
    "        # return x\n",
    "        return F.log_softmax(x, dim=1) # log probability\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torchvision.datasets.mnist.MNIST at 0x7fa362b9c7b8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_data = datasets.MNIST(\"./mnist_data\", train=True, download=True,\n",
    "                           transform=transforms.Compose([\n",
    "                               transforms.ToTensor(),\n",
    "                           ]))\n",
    "mnist_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = [d[0].data.cpu().numpy() for d in mnist_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13066062"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30810776"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28, 28])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_data[223][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(model, device, train_loader, optimizer, epoch):\n",
    "    model.train()\n",
    "    for idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "\n",
    "        pred = model(data) # batch_size * 10\n",
    "        loss = F.nll_loss(pred, target) \n",
    "        \n",
    "        # SGD\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        if idx % 100 == 0:\n",
    "            print(\"Train Epoch: {}, iteration: {}, Loss: {}\".format(\n",
    "                epoch, idx, loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test(model, device, test_loader):\n",
    "    model.eval()\n",
    "    total_loss = 0.\n",
    "    correct = 0.\n",
    "    with torch.no_grad():\n",
    "        for idx, (data, target) in enumerate(test_loader):\n",
    "            data, target = data.to(device), target.to(device)\n",
    "\n",
    "            output = model(data) # batch_size * 10\n",
    "            total_loss += F.nll_loss(output, target, reduction=\"sum\").item() \n",
    "            pred = output.argmax(dim=1) # batch_size * 1\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "            \n",
    "\n",
    "    total_loss /= len(test_loader.dataset)\n",
    "    acc = correct/len(test_loader.dataset) * 100.\n",
    "    print(\"Test loss: {}, Accuracy: {}\".format(total_loss, acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 0, iteration: 0, Loss: 2.283817768096924\n",
      "Train Epoch: 0, iteration: 100, Loss: 0.6110288500785828\n",
      "Train Epoch: 0, iteration: 200, Loss: 0.18155980110168457\n",
      "Train Epoch: 0, iteration: 300, Loss: 0.31043028831481934\n",
      "Train Epoch: 0, iteration: 400, Loss: 0.518582284450531\n",
      "Train Epoch: 0, iteration: 500, Loss: 0.1202855259180069\n",
      "Train Epoch: 0, iteration: 600, Loss: 0.0989612340927124\n",
      "Train Epoch: 0, iteration: 700, Loss: 0.09637182205915451\n",
      "Train Epoch: 0, iteration: 800, Loss: 0.13470694422721863\n",
      "Train Epoch: 0, iteration: 900, Loss: 0.06548292934894562\n",
      "Train Epoch: 0, iteration: 1000, Loss: 0.03107370436191559\n",
      "Train Epoch: 0, iteration: 1100, Loss: 0.03948028385639191\n",
      "Train Epoch: 0, iteration: 1200, Loss: 0.09810394793748856\n",
      "Train Epoch: 0, iteration: 1300, Loss: 0.15199752151966095\n",
      "Train Epoch: 0, iteration: 1400, Loss: 0.016710489988327026\n",
      "Train Epoch: 0, iteration: 1500, Loss: 0.005827277898788452\n",
      "Train Epoch: 0, iteration: 1600, Loss: 0.0754864513874054\n",
      "Train Epoch: 0, iteration: 1700, Loss: 0.012112855911254883\n",
      "Train Epoch: 0, iteration: 1800, Loss: 0.03425520658493042\n",
      "Test loss: 0.07333157858848571, Accuracy: 97.71\n",
      "Train Epoch: 1, iteration: 0, Loss: 0.07740284502506256\n",
      "Train Epoch: 1, iteration: 100, Loss: 0.018157958984375\n",
      "Train Epoch: 1, iteration: 200, Loss: 0.006041824817657471\n",
      "Train Epoch: 1, iteration: 300, Loss: 0.1392734944820404\n",
      "Train Epoch: 1, iteration: 400, Loss: 0.022600188851356506\n",
      "Train Epoch: 1, iteration: 500, Loss: 0.020594105124473572\n",
      "Train Epoch: 1, iteration: 600, Loss: 0.031451016664505005\n",
      "Train Epoch: 1, iteration: 700, Loss: 0.09078143537044525\n",
      "Train Epoch: 1, iteration: 800, Loss: 0.013186424970626831\n",
      "Train Epoch: 1, iteration: 900, Loss: 0.04006651043891907\n",
      "Train Epoch: 1, iteration: 1000, Loss: 0.014285147190093994\n",
      "Train Epoch: 1, iteration: 1100, Loss: 0.22637280821800232\n",
      "Train Epoch: 1, iteration: 1200, Loss: 0.02185329794883728\n",
      "Train Epoch: 1, iteration: 1300, Loss: 0.13519427180290222\n",
      "Train Epoch: 1, iteration: 1400, Loss: 0.021606311202049255\n",
      "Train Epoch: 1, iteration: 1500, Loss: 0.016718149185180664\n",
      "Train Epoch: 1, iteration: 1600, Loss: 0.07150381058454514\n",
      "Train Epoch: 1, iteration: 1700, Loss: 0.041178762912750244\n",
      "Train Epoch: 1, iteration: 1800, Loss: 0.004264324903488159\n",
      "Test loss: 0.040256525754928586, Accuracy: 98.61\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "batch_size = 32\n",
    "train_dataloader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST(\"./mnist_data\", train=True, download=True,\n",
    "           transform=transforms.Compose([\n",
    "               transforms.ToTensor(),\n",
    "               transforms.Normalize((0.1307,), (0.3081,))\n",
    "           ])),\n",
    "    batch_size=batch_size, shuffle=True, \n",
    "    num_workers=1, pin_memory=True\n",
    ")\n",
    "test_dataloader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST(\"./mnist_data\", train=False, download=True,\n",
    "           transform=transforms.Compose([\n",
    "               transforms.ToTensor(),\n",
    "               transforms.Normalize((0.1307,), (0.3081,))\n",
    "           ])),\n",
    "    batch_size=batch_size, shuffle=True, \n",
    "    num_workers=1, pin_memory=True\n",
    ")\n",
    "\n",
    "lr = 0.01\n",
    "momentum  = 0.5\n",
    "model = Net().to(device)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=momentum)\n",
    "\n",
    "num_epochs = 2\n",
    "for epoch in range(num_epochs):\n",
    "    train(model, device, train_dataloader, optimizer, epoch)\n",
    "    test(model, device, test_dataloader)\n",
    "    \n",
    "torch.save(model.state_dict(), \"mnist_cnn.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NLL loss的定义\n",
    "\n",
    "$\\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad\n",
    "        l_n = - w_{y_n} x_{n,y_n}, \\quad\n",
    "        w_{c} = \\text{weight}[c] \\cdot \\mathbb{1}\\{c \\not= \\text{ignore\\_index}\\}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 0, iteration: 0, Loss: 2.2915596961975098\n",
      "Train Epoch: 0, iteration: 100, Loss: 1.0237065553665161\n",
      "Train Epoch: 0, iteration: 200, Loss: 0.840910017490387\n",
      "Train Epoch: 0, iteration: 300, Loss: 0.7526986598968506\n",
      "Train Epoch: 0, iteration: 400, Loss: 0.9580956697463989\n",
      "Train Epoch: 0, iteration: 500, Loss: 0.6261149644851685\n",
      "Train Epoch: 0, iteration: 600, Loss: 0.4255485534667969\n",
      "Train Epoch: 0, iteration: 700, Loss: 0.4818880558013916\n",
      "Train Epoch: 0, iteration: 800, Loss: 0.731956958770752\n",
      "Train Epoch: 0, iteration: 900, Loss: 0.45393142104148865\n",
      "Train Epoch: 0, iteration: 1000, Loss: 0.7139236927032471\n",
      "Train Epoch: 0, iteration: 1100, Loss: 0.4227047562599182\n",
      "Train Epoch: 0, iteration: 1200, Loss: 0.23375816643238068\n",
      "Train Epoch: 0, iteration: 1300, Loss: 0.4680781960487366\n",
      "Train Epoch: 0, iteration: 1400, Loss: 0.352077841758728\n",
      "Train Epoch: 0, iteration: 1500, Loss: 0.36358141899108887\n",
      "Train Epoch: 0, iteration: 1600, Loss: 0.46214842796325684\n",
      "Train Epoch: 0, iteration: 1700, Loss: 0.4750059247016907\n",
      "Train Epoch: 0, iteration: 1800, Loss: 0.4483456015586853\n",
      "Test loss: 0.45549455399513245, Accuracy: 83.48\n",
      "Train Epoch: 1, iteration: 0, Loss: 0.46502870321273804\n",
      "Train Epoch: 1, iteration: 100, Loss: 0.4504859745502472\n",
      "Train Epoch: 1, iteration: 200, Loss: 0.5228638648986816\n",
      "Train Epoch: 1, iteration: 300, Loss: 0.507514476776123\n",
      "Train Epoch: 1, iteration: 400, Loss: 0.33425623178482056\n",
      "Train Epoch: 1, iteration: 500, Loss: 0.15890713036060333\n",
      "Train Epoch: 1, iteration: 600, Loss: 0.4329398274421692\n",
      "Train Epoch: 1, iteration: 700, Loss: 0.47604358196258545\n",
      "Train Epoch: 1, iteration: 800, Loss: 0.40596315264701843\n",
      "Train Epoch: 1, iteration: 900, Loss: 0.31725335121154785\n",
      "Train Epoch: 1, iteration: 1000, Loss: 0.5835919380187988\n",
      "Train Epoch: 1, iteration: 1100, Loss: 0.3334502577781677\n",
      "Train Epoch: 1, iteration: 1200, Loss: 0.3043973743915558\n",
      "Train Epoch: 1, iteration: 1300, Loss: 0.3891294002532959\n",
      "Train Epoch: 1, iteration: 1400, Loss: 0.20209042727947235\n",
      "Train Epoch: 1, iteration: 1500, Loss: 0.26769235730171204\n",
      "Train Epoch: 1, iteration: 1600, Loss: 0.366751104593277\n",
      "Train Epoch: 1, iteration: 1700, Loss: 0.16336065530776978\n",
      "Train Epoch: 1, iteration: 1800, Loss: 0.48901161551475525\n",
      "Test loss: 0.37672775785923, Accuracy: 86.15\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "batch_size = 32\n",
    "train_dataloader = torch.utils.data.DataLoader(\n",
    "    datasets.FashionMNIST(\"./fashion_mnist_data\", train=True, download=True,\n",
    "           transform=transforms.Compose([\n",
    "               transforms.ToTensor(),\n",
    "               transforms.Normalize((0.1307,), (0.3081,))\n",
    "           ])),\n",
    "    batch_size=batch_size, shuffle=True, \n",
    "    num_workers=1, pin_memory=True\n",
    ")\n",
    "test_dataloader = torch.utils.data.DataLoader(\n",
    "    datasets.FashionMNIST(\"./fashion_mnist_data\", train=False, download=True,\n",
    "           transform=transforms.Compose([\n",
    "               transforms.ToTensor(),\n",
    "               transforms.Normalize((0.1307,), (0.3081,))\n",
    "           ])),\n",
    "    batch_size=batch_size, shuffle=True, \n",
    "    num_workers=1, pin_memory=True\n",
    ")\n",
    "\n",
    "lr = 0.01\n",
    "momentum  = 0.5\n",
    "model = Net().to(device)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=momentum)\n",
    "\n",
    "num_epochs = 2\n",
    "for epoch in range(num_epochs):\n",
    "    train(model, device, train_dataloader, optimizer, epoch)\n",
    "    test(model, device, test_dataloader)\n",
    "    \n",
    "torch.save(model.state_dict(), \"fashion_mnist_cnn.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN模型的迁移学习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 很多时候当我们需要训练一个新的图像分类任务，我们不会完全从一个随机的模型开始训练，而是利用_预训练_的模型来加速训练的过程。我们经常使用在`ImageNet`上的预训练模型。\n",
    "- 这是一种transfer learning的方法。我们常用以下两种方法做迁移学习。\n",
    "    - fine tuning: 从一个预训练模型开始，我们改变一些模型的架构，然后继续训练整个模型的参数。\n",
    "    - feature extraction: 我们不再改变预训练模型的参数，而是只更新我们改变过的部分模型参数。我们之所以叫它feature extraction是因为我们把预训练的CNN模型当做一个特征提取模型，利用提取出来的特征做来完成我们的训练任务。\n",
    "    \n",
    "以下是构建和训练迁移学习模型的基本步骤：\n",
    "- 初始化预训练模型\n",
    "- 把最后一层的输出层改变成我们想要分的类别总数\n",
    "- 定义一个optimizer来更新参数\n",
    "- 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Torchvision Version:  0.2.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, transforms, models\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "import copy\n",
    "print(\"Torchvision Version: \",torchvision.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据\n",
    "------\n",
    "\n",
    "我们会使用*hymenoptera_data*数据集，[下载](https://download.pytorch.org/tutorial/hymenoptera_data.zip).\n",
    "\n",
    "这个数据集包括两类图片, **bees** 和 **ants**, 这些数据都被处理成了可以使用`ImageFolder <https://pytorch.org/docs/stable/torchvision/datasets.html#torchvision.datasets.ImageFolder>`来读取的格式。我们只需要把``data_dir``设置成数据的根目录，然后把``model_name``设置成我们想要使用的与训练模型：\n",
    "::\n",
    "   [resnet, alexnet, vgg, squeezenet, densenet, inception]\n",
    "\n",
    "其他的参数有：\n",
    "- ``num_classes``表示数据集分类的类别数\n",
    "- ``batch_size``\n",
    "- ``num_epochs``\n",
    "- ``feature_extract``表示我们训练的时候使用fine tuning还是feature extraction方法。如果``feature_extract = False``，整个模型都会被同时更新。如果``feature_extract = True``，只有模型的最后一层被更新。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Top level data directory. Here we assume the format of the directory conforms \n",
    "#   to the ImageFolder structure\n",
    "data_dir = \"./hymenoptera_data\"\n",
    "# Models to choose from [resnet, alexnet, vgg, squeezenet, densenet, inception]\n",
    "model_name = \"resnet\"\n",
    "# Number of classes in the dataset\n",
    "num_classes = 2\n",
    "# Batch size for training (change depending on how much memory you have)\n",
    "batch_size = 32\n",
    "# Number of epochs to train for \n",
    "num_epochs = 15\n",
    "# Flag for feature extracting. When False, we finetune the whole model, \n",
    "#   when True we only update the reshaped layer params\n",
    "feature_extract = True\n",
    "\n",
    "input_size = 224"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读入数据\n",
    "---------\n",
    "\n",
    "现在我们知道了模型输入的size，我们就可以把数据预处理成相应的格式。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_imgs = datasets.ImageFolder(os.path.join(data_dir, \"train\"), transforms.Compose([\n",
    "        transforms.RandomResizedCrop(input_size),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "    ]))\n",
    "loader = torch.utils.data.DataLoader(all_imgs, batch_size=batch_size, shuffle=True, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_transforms = {\n",
    "    \"train\": transforms.Compose([\n",
    "        transforms.RandomResizedCrop(input_size),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    \"val\": transforms.Compose([\n",
    "        transforms.Resize(input_size),\n",
    "        transforms.CenterCrop(input_size),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ])\n",
    "}\n",
    "\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in [\"train\", \"val\"]}\n",
    "\n",
    "dataloaders_dict = {x: torch.utils.data.DataLoader(image_datasets[x], \n",
    "        batch_size=batch_size, shuffle=True, num_workers=4) for x in [\"train\", \"val\"]}\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img = next(iter(dataloaders_dict[\"val\"]))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 3, 224, 224])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FV1UjGvRM+tpolTAWGkynu6GZPJVCn9+BzxPz0zOn2wvfu7ySx16+8Ir+Fo7HlzFNBx5p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lspYLf7maBVNraV02nREj67li9jx+dN0U7mlZSUa3c9mTT9LviZV8/Mk9DJ35GP1nv0fGMXDj\naSrKT8VrSdO7vJPRE35FpmAjhY1UcNQ0OKo0H3pZPK2QRYmfN8koF1GZxYpKEmGHtpTirIsaePeI\nZPPcEeQ+aGLMyDinn/YQpi15I5Whw9N0tFsUsj6ZrMQLNMgsRUdwFJNFK9tJCIM+DlxQEyWRyKNQ\nKIosuG0g+V/XM6Y8zhstaS68Kczs5+qQMQvhGLyacjEMm/6V1UScMPE43DIpyfasJmnCiJBmyVoY\nM6Kcir796Vc5ljFL7gSzlkN/UrS6Llkflm3YwnnDr2HNvJfp4WTZunIXHSpJ/KYrGfRYPbGB1ZhB\nGf1rTmFPyscpq2bhs7NYukExYGwdQ/paiCM++d278JtacexeXDR3EsT7c6CQJF49HRnA0gW7cbAI\nxyJEwuDYDt0pMRQINEhJoEokpUw2h++6fOodI3hnsnhpl0xLM27apzmbJZP2yTRliZdbHC20cDid\nJnfwIJn9BXLNksOtLrnUQTKpNK1NJTCN67o0pyVtuz02v3CE7Q0exjkWTnk599UnOfzG9cxK2SzI\nOAgEHhC1NT0Cm3cLMK7aZk/Ly5w84Gp+MnkXox4dT83UJOq4nmx74nqs5Dq+GHAP4+aO/s7x+G+x\nUgj0t2hfkhcGWmuCAIpKoYoadAnNbWlJFhtlZCmi0YEiUjTIoPkK8DGJC4uMLtlFmRiEdRiPAM8s\nQTMNAqQhQET+E95SGmIIYzgBUmpEAaQZw1A+mJq4Fea4sjjajHFasZLoiAz3961l66YWfh8sxw40\ni4cNJr5zFT/ccRy3n3wPb15bjYq7COUQint8fcI+xh29jqhlMWtTmvvO/RsyP43w15PZ37CMFw+c\nxxOjy1GdaUJlgtTRFp5S52OEPSiUklqHKtDdNvlUa04MfL7SWc7EJiNy9ImWkU65FH60iQkNGjWs\njosOx6lQg+mzaAPX1fek+aDiaJtBxvNL/Xut0dqkqBV5M0bIlkQTBusbM8jibvokYyxrfIY5T9Ww\nTVvcVS1JDqzkQEGzdO0rPNqeovfPKomGHPIyT15Knn2ziW7Nafxcih0TXaIqzmPzL2fBtoHM312H\nfd6v+KpKcZWoYdSAgYy6ezdPbq7linm1xOPTWffOC+TyATnZwqWLX2fE45/yrAAAIABJREFUySfg\nZQ8jm+ch6m7CF3mcMgerdj3RsXkiYYOtTR7bJtxJxc8SnHHmWiaNjJJraqBP33pGzx3KgV2bsMN9\n6V01no7GJnIyzgw7zrbGHCeOLW1VIzqP8E2ee3YNbS1b2ZfbT2CB4YHKwxcmEBRQKk9HXuGpAFok\nXgE8LSgGJZVuTisomgyPCJ5LuaQKOSgqPGVwVGtyOY/MEZcOr0RS8vws0hdov8CBg5qNb7pIV2Bj\n0ivpcOLKtZxXW46TjvNUSwurUg5JYizpu59tXoll2j/uMK85YMnQeq5/ZSZfjXJInPoGdzxwECPZ\nwDn9z+XQr2/n3tYId37HePy3SArffF2Nmz+AVBFsShvvgBKhS1JaJ8ggoCgDDFkaRpBCEOQBZXAi\nBn/R4FsSK19y/3FFQDQQONogMI4Ze1oG6AClwjjaRBJgBRpPG+iiwPAVUuQRGjyjRGKKxhL8oLoO\n1xCU/aiV5sIJyF+ey033jCY0uYhlggo8bthwEOes85lx3QQyp9zCrMZW1jtpBtR4jJvgMHrWrTz5\n2X5GD5uIkX2ZM086FbxxvG/OY8SZNzDwpBm8tbCWiXNvAV2HKWDEoEq2LdmMqq5C+BpT2bg5nw8D\ng1McGzCIRgQdrT7BGY3c8XyODjfKmNrBVP/oQyq/fQar3CLlFfgoD6kWF1eZJQs+DVJ7SDTdHQcM\nTY+eMS6sEsxa3E73LOSqFL+4pYEBSYfQQeg/t5GfC8hpzZVDq6moSrC9KcVhv5OQsOnw27ll8Wqw\najhzssOhgsC0czz7wkoOZ10m19aw7MXBTLz3cuR+n1G1NdS+soI+gyeyZO06un1+HOan7zNl5nJ8\nP8uhj//C0YxHRXIYz27qpH/UI1lVRWD0g2AueV+wo0FQPPUpliyv5eqvF+Kly0DG6D23hni8jgnL\nVxKJV6E1tHpJplbVMzPRnUXnT8eY38SQoXHWLH+WyvPv4dIJn/NsP8Vln5zHFwddTgkHfJVTFJQi\n1yk5pHwKErThorVCGmHAwCtqKPjkpKS305/zzACZyZArFPgmK1Fo/CJ4WYXvG0hdRPkS7ZtIX5JJ\nC9a8kmJNYw5LF5hdX8ee9nZcV/No1mPD19dz4WYYMrLAqN1Rhq5YxofvteN1L/K9s5bwSkszf/i2\nhTsv382yKwIyC6pJfGDy4JVDObLxfM7I76T1Uxu6fbd4/LfYPnz19VfkOrMUixlUUZbcdeUXGEpj\n+D5aFvGUxiqUbLhDBY3UPnk0nqn4QgtCBShSqhMUTJOIMpGmRgmNFBamsOihSwCMiABlCaRlQjjA\nOe5jXnvlbSZO+rCkm7ACDMIMr56FURjKixsOkpMxLjr5IGvm/J3hk5L8+tMroNPAIqDfiJs4b9w6\npt2UpH91lEUPTSV++k7eXnQTn+5bRf/4y8x5o4PGd+fw0fL5hL86xD9OuJjtC/6GQxOf/uM1fnrm\n+4yo3Yb2G+hjbUKaPqGoSbxM0sOCSNhAKh+FICo0eQcq4uUc2iowfryF9btcKkQI1al47NGhyDKD\nimECFTdwfUmgDfKEUV4nWpWEZiqQGKJEFu4Rj2InBIk4jBic5MqqanxlEK8sJ1Qe40BVOSIWwwqX\nE0k69KmPI6KaRNKmX20Zlh0QtcMsakrTNXsThzDADHB9+Djr420NqEg7XBMbwYjkSMbccTcffrGD\nHw1fS+r1F7lv/JW8+dyveH9VhvVzV/Dq7CKP3LebiMhSOXQED+54iv7DxmI4vZFAriVLn7qBjKm/\niXdeu5ztWxSfH34V00gSr69HZpt5a/FMtu1vxgw7yEIdr/35x3w55EcMGDuMAfXT+Yzn+Cz9GB+s\n7ctxf1tPD8flZ9+/jNfeTHOWU4OpILVrL/v+lCOTkxQkHJUgZYmrGMgA5eUJjrSXbOQ7BcOTJh1+\nM7n2LMW8JCs1ylPITh+/UKBDFpFBADoLKiCVFsxfvZ81DU0IFRCPVzOgOsZ2VxIXBkVlMKUxRc2w\nSj7ZN5e3spoZD6Y4O1nFx8f15BcLDlL/+BM4qVM4fvUvOP37y/nN1+9xcp/FNG+YzcaVa7HDksA6\n+J3j8d9ipfDPb74i57rYsoySEMAkCLxjHAUPQ5Um6TWSvK8pUMBSmgK50nJfeeR0APmSkWexACHT\nhKAEsAgZAUEYQID0MY+BWiIFQc6OsbA6YIj/DIMueohLrr0Wy3qdimQlHzT8Dd+Is7NpCP+84gjZ\nsy9gzVPv06PbJ5x6isOU275gb+Np9P/RJOZvmccvT/sY94dxzoxJ9j6/kAOimg/3KGZeUMtPvb38\n/f6vyN92O8/c+AvyyV50t26CD44SLf+YB277mIrkDrZ57Xyy90o2N33Boe2KSwY9SM5SqFME+Xwn\nUSdKDyGR0uKN1fs5b1SavSlN72iMt2yfQeXlnOv9gFzWo/1EqLEhnwuQnk9Ia0xRQv0oJTF1qbXo\nWAKtDKQ2yJgGvZLlJJRCmxIjHCeZSDB6MGjLxCAgblucYhhYQvNRzMI0I0gdMHdyGjtuUTnSwBJh\ncApkWrL0cmCpu5ULVj9J84FPidZ0R2cM+sQuJDH+VuaLNJnoW0SSPTgUS/LbPa/j/dnltkcHoV91\nePFJlzXtWeYZA4hKj97zH2LvLnDOqeHQvoeYdtM2Mi0p3t39MlKUMWXWPRhqP8/OuJ0ecyqZtmgf\n/SdsRDhh9jzwDtsX3kd82Ek8Of4nDJrxIOMm3UNFdQ1eeisvx4ZyYPN8lnSbwIsLnsOYWgPSBQmm\n0FiUbAdCx6DjeRmQ91w8Wc2Yc+LElaKjXaBR5GyB9BUdCpSCnMohfYOCCtOaEezY5fHiKy6rXmon\nYltINMMHVzO8uYU1TYoeVXGwbDrKdvPXQoGvYwu5orGZq96xkF9P54R/5nn0m/f4et6DXDHvMV5h\nBbNefp+Jd8TZeEsV/2jbxPHJB/GNAMcqhy++Wzz+W6wUvv7iH6TcFlr9dnw/jSpkKUqfvJRkpcIN\nPGQ+oEMpfCU5qj08T5EvGHQoiZQCP+/hoQh8MLRJXoHSoRLGTZnoQql2hGGX9AaBIC8FAzzFT8+/\nnXXv/5nnl93MA9OLBFnYk3qX87+5mOUDT2TOw9M5s9dk4nEHVBOPPrCfayYl0cf1xHH6InDQKk9F\nIswPJo5j6+7F7Gzfxat7XyF8wjlYNW9zySVjGF3j8PybL1JZl2XGovOpvuMWcrkGNi7sznU3TMIP\nXqCi/m3G3PxTaHdRrkEIuCxZSb+ySiqdKsjAvtc1KxbmaN0AF0ZjDKh0iJZHuXJgkoq4heUXSKV9\n1r+5H1SRo4UwecA0JAoLjJLfQDRiY5uCiMkxkrYgYphIT9Ka9RGWRYUBRliCYWNgIbTALRT5qDWP\nNmwsFCFlohQkBkfpkbSxbDB0J9ISHMpp+vSsJjliEB1Wiug1F/JW03YStTGkOMqcwXVE66t41vdY\n/+4qVi14BmmW432+neWLJtDaGUbavdm8YR/71hYgXkk8VsviFdMZVDeU1167lQRZRl1ayZ5Gn2k3\n30RFPI4hTLY3wcSly4haYIjSdqv3yKkMmFpPKDiI70Gk9iZufO106mbto/fAasIDzuHu7W/zxK83\n83HkauJnV5H1AvKiJNKPGmFCGnIqIK80WTxUUPKiGGSbBK5EeRrPA9fz8P0CvlJ4QYD0HNy0YPMW\nxfzHDjJ3TiM7G9NEhEneF0y5tp6v/r6MGx88ytnWuZzuncafd3/I5cNfpFvXLGbcW0u/qt5c1fgu\nj/74PlaOb2T/56eTOe8aJj9jcsaAvzDvjY+4+cfz+GDrXWxv3oUWnWhL4dj/N6XBf3v8W6wUzuhZ\n4KUN6+hVlSCbiBGPJYjYcUxlktcGPgozK5FBJ9ITuMrn84JP4EsyniafK3DUNwh8Ay/QWNokbxhE\nAA9KqHM0RsHAwsAyLZT2icYUOwYpvlyRJPnZVn5f/QD/OGU1uWwcJ5bln/0aePH5boyxJnLr2Gpa\nXjGpqK3jxLO/YOOG2fztwDiuG3s5jz00n88+uppfrDQ5kF5J0XfJeJBpSnHpkn9wSuFS0v0G8Ez3\nUzh6Vx9WTemD+PBcdN8PCaKbafO3MOGi6+g3cCwzV52Od9pETur/P+jYM5dPn/mWPnae3765i4wZ\nx9gEhzoFcSI4SehwJNrOkvZcLiyvwxNFcs2t9Br1PNNn7+ajt6uIFkEUiuR0gGOY+NLHVFYJc29Z\nWGhkQUEQYMsAXVAoCRHTQZeZGIaJMEvmvlJrcl5AuqXAhylBruAhhYUwPK4cmcQveDimRtsa3SlY\nWlfJRycsY82It7l3/GK+6Lwcpc9l7pzb0XiMe3ohM8ZOYkxtjBc3rKZDOwyPQ+sGlyQ+PSrjVA5O\nknpnCP2HjWTA0KGk/qTIxPN4tkWixsdLNeCmHMY9v4gBi0bQvOkVrps2G+H3p278PUR/dhNWpaDY\n8jiVfeuZxpfIYDPOyI0MmbuGiBNDZ7dwqPNBei99mJVOLe/OOI1U+o9sW3wTzgUmxQIYJigjQGow\nAxNPg/QtLCEZYYCXTeNbCnRnCbmmfJRpkeuEvak8G3dl2diU5eTdOea/oDAwKYZNejhxXp79Q2Za\n7xOP5Hjl1SR3zNjEji0uvdvStOzKMCiueeXROMH7UU79Y4x9ooGtp87gLxcpmgeHeGzJrWw9cCG+\nEaPhqTPZuul1nAgIHAQ2ypDfOR7/LZLCZ59W8PDy/SxNHqGifxm9q6L0qi4jHrMgiKOkgfQ1MvCR\nnqSjIHFzBZSvyOTz5P0A6UOHX6pMKgNMS6AKJU6jNGxCgcL3JJYl8HJFHAcO+REGNAccun4X0QmS\nyHvdWbfsTuKFfSgBK7ZkuP2yNvQ/vuC4nSGmVA9lzKzLKf/BS4wp30Bu/8v87IKxLJif5LKFko6D\n+/nG0GS9LNKT5As+G3c9Sb9fwv2Lt6JXevQbOIpYrjdvzUwy6I3b2bm2nRO3L2TjCxfTe3yaJVe3\nkdCvs2jBSzS9fREXLW9hm9jN0ey7/Gx2lL/sGEo/HI4qUHYC29E4VhZDt6AEVEYjnD1yMVaijCce\nHso1Ay2CAhg6RCTw8U2wsCjaAjuwCBuKrAzIK0kxL3B9jev6JOIOpjCxQhaOCKEogJSlycuCjyEg\nl8pg2ZSSitaoiAlCkC9oIgZgKmYsb+bSp+5kwdJJPObcz6EJ69maTbFtv8JTMd4a3JcdDa/zXNlT\nDO/bm1uWbwIsBo2/koh/ALwWRNajJlZPv7o6nFgNK7Y0Q5lFwrHolczQuzyJqJ2A73/ETE+waEOe\n6NrpDBg5ElcdIfNmLcmR1aj2BqI9x8PgNdhGhsDwMY0q/KZ5pJ7vYv7iswgOvooqH8GKlx6j8d5/\ncskfTyIwwQrCdBQKpX6l8slKOOwpPF+TEFkyJLHMMkK+xrQVWDEqqkYQcsrZ07SbRZsaOXdDihVr\nCwSIYzwFgfYNTvh2I1P65Mht2cPxV7zJeT9eyzYp8Dt9Qp0+MQKyMkdQ6KSYlQwoN3lvfiWfeAke\n3vI68XgNl0816XHSudww51m27V5MPAKaEAEBjukh/l+E+r9FUoh/VuS5J5rplXRIPGRTWemwoK9N\nRcLGiUYQpk3IMNEE5F1FxtN05CUF30enDXIKdKfG8xRWAELYSOUjhEBiYXqSvPThG4+/cjXDLl5O\n5v0WQsE0mtsFp9yaYuc5NyK313Dj4D0c5y9j6iunEXF8HhjsMXvoYHrHVvPX829g0S1/4k/Vtcye\nehyyaQ/HfXUlcxv6USi0EMHgB0h+5+URwiDU0+bjtgzp/XEidpRPXruf628dSaKshrNPrWXK0UX8\n46krGTD4QSpnmWQOjmLGyd254rpRLLk7x8brXmCnyjB/QyOTBxqkLs5zxxyP5x7pyadECCxRciQW\nVQxAsT0tcQPBJbsb2HLRdG51qrmqZhPrMwoMH8IBVidoU2AbNkop8nlFXmm0EvjtafY0eexsCRg9\n0KFPbQhLhPFMjY3AU5Kc9PAVOEqT9xWyoLBsB2UERIUDRMkZGYpZAwzJtFl1WEcH89i02aTbYgzZ\nWceN8UGMqYmR8QIqBr/MoZsFuSbNmPo1LOBx1j0f5vn5j2OHZ9L60nTc/VmGVFZix2sQ5dWE7AZM\n45j58DnXIw2BQR15lWXN8q1UDIwy8eEVaP0UexffyJ7md9m4uMAQYuA8jNnZQBCz0SQhfR+bp/Xg\nsdm3csWMH/BS2Wcs+83d3FjzNLfMdrGtcoxOB6e8iqXTf8Xi1WsZNmM0beogSoJjaKK2RaZcEzUK\nRBDE+tbRr+ZOKgdN4tyaQWjb4pqjZXz897t54LlxLJ23Gz9rYQRgCEXv2hQdp9Xz1z+N44En/P/J\n3NuHOVWde/8fdO0269SVmm3ZUxOdoJlK1KHOVIYCFRBQUEBF0Sq+oCL4UkrFN+prLaJUKaKIFl9Q\nKaKIiCIiCnRABs+MAu2MZ4INNlEybaKzsTs9WXpWdC/l+SOc63eu6zm/Hq/zPOe5ev8z2Tv7SvaV\nybqvrPv+3t8POFHingEhKKcSeHskx7sBVitMUlP2eyn5ZaSo8NhVMO6f07y1cDVH/zjLBv0MIqYw\nThkhQwg1MupxQH3xteIfIil8/kXIpvYcz3Zq4oki9Q2dpBuj9L9X0T+dwK1TxFSNLG2MRQaCfFZj\nAk3ZCMIi7NMaTIhxPVK6tn8KrMARAQoIRcjm++5h6qR/YsRPX2dbx3y61qUh2U5/oRDT13PMcUuI\nOldy7n0Hs++TBP0TkkPT7zHsxmsoDf2K/iOW0HDCDBqjD3H+VeNYfOsEvvW3jdC5A0eCrZO4VnPK\n2Bb2Gc2ubJZMJkusEKe6eArzxqUo5VvZVEpwS32WM6+6hDuffZ7DBha4+rgvOL+zkymnw1VrGxjc\nuQPpuAS2wsB+UbrCKEtbYLBj+KAiUHWSMDCUZQLr55DuANKuT6B7mTu1hZFHfYQnx3JpYxtOVxIv\nKGINdPkByoUQg9WWMHTQvZrAh1IxZN36HH0bFPVelKZEFMeG2F4oWJ9yYAjCgJ6SxSXk+MaGmqEp\nAV7UwaDQVuM5krBOUTKWQwpFFpQFD867kcIaj+1hO8ZkcLOWpkQzc9rvZ9LQn/OrW+4mdcd4nOLT\nLGlby71TR5AalMJEFYVtXVzgP4RyJhBRDVw+awIRnUO4Q3FT52KMT5wC2x+ZzZ71BX793mKSbiP5\nnGTUHR8QCzeineU0Lb0Eh22EpgFHj4c/3smux8/B33Y/XfoRDupexJr1p3FPy19p+mEreZXipfcP\n42+Rr5h57bfZ8k+H8nlDX4alz6VjwfVIx7CHgE14pOIOyTqPeldj3TJ9vQ5kh+S9puc5YXQLs6YM\nw+zaxN/+Npy5SyZy8zUrqRYNOFG6WrM8uvRxPjJxhIJDpMXFEnoV6oFIYwJZ7ocJDB+KIoeUBOW9\nUfyE4W1H8KvpLvRrJawDz22gpAuY0EMKjXAFpib0/9rr8R8iKXzZDKbD4IteAsdhU7GEm1XUey7J\nRI54UuC5EiGjuFIiQ4NvK4SBhQBKYU274KooHprdQIw6rAypSgcRKpCWYbct4jC3wrx+l/L5N5cw\n8LRvsrvzRMqmNiXZ/edJbHvmVOyAdxl12KNccWI9/Q4dyIZ2iZ97nyHq+5zdVE/3nOu58Po3ueXq\n87lx5nqUKhITEoICgSsOVKghKSP4CcHM4XV8vHQXK6Y8zoK5C7ijcTIjBxne/KzKqx9/m5mzI4y6\ncx7bHxQsm30aImeQfjcxafBSMGTyRKRuJPZqQGmhQ7wqMMqhKhxsYDEiii4EaG3o6NZMv7aZUUd/\nzpRfZAnGrOQEdwZ5M4KPyOHZVmRQG44RRpIvBshCyJ6CZk/Bx6x28KSkSoVqBFBQzBfZpzyMH5Av\n+sRCQaZd0L/RI+6BtQU86aFDU7O/qwj8iEM86nHr8k5OOLWZrvcTXHbVeEIznPmtv2FN7w7CMM7s\noQ/hixKbl9/D1oY4jZzLrVcu5hqdJdtdopQRFDLP8Mo5Qzjr5plc/PBTRJTAkwHJtCAWlQjHR7ev\nxJ7XyaOzJpNuPpf4oNMYcgXUp9pxh3owVFHK3ovOrcQWoygRUmw/lO1v/IZM8CLzXz6avk4Pmbem\ns/HY+5nmzePcaTcy/yeH0DJYcu/iBPm1h3DkhsVcdp7LzBvn4+siu7a1I7IFHBPBUESHScqFTnar\nFrQM0R0Z1jUPJT3kIY4bOpTrZvTnpjvv4WdHjeWhW9eiixXueKCbaz4cSih9wngCqzRKCBRgpSbm\nCmR9DIhwkvEYuM8wOJmgVAjQjRMY0Hk2h49uQG8r4iAJraglA5kkLgXWGHRo/n9W3/8e/xBJ4aA+\nIGsGB2gjEMpgezVB0WFrNkdfJZGeRURzJFxFGGqwBfQBCnUMB0dGsCYkMBIpDYFTRRHDYoiEFWoa\nR9jc7TLuuCbOOnYkDcMO4ehTDdKpkXtmzd3IY1cEvP03zQ+aFnLYD37GUd+WbPXTHHXUoTzx2JOk\nE2XeuP2n/HOunmJnCxvWh/QEkqoBHAVYjKp5/kvpkIho+pw4mgFf/IESG5n91wFsuGkqHU/dzVni\nZTbtmsGOhWey54ZR3DL1PBaPDPEcgTJQ7Q1haBKsRHu1PajSASWbqBWpQoAAJSUiZjF/rACaB65t\nQL+wne3tixg5/RrGpAxLOkHIBCpWh18OiIcue4Je/MBQKvoUCpqdOR9GpBiVThKKAmF7N8N+MIBS\nMcSvZOkpF5ARj1LVMPOnM1jfcTShdNm/73Mevn4AodlBRFiIJtB+QJWQ0BoKuTaGHNPMiseWMe2q\niXidKZTppiuxk2vOa6aaSxKISfTkY2zpbGdURjG4YSgtAwSvLJc1eW9nO2bnPYhtI6nWDUelp9DX\nKZBQBfy21QhjkKMvIZW8Cq/fHIinoJTHS6SwSY3uHEjXy9cg2zMUWp+iXNzFZnk+WX8Xd80dxtnh\nBv71hD9y9+ITeeOo4/jdj45j8n2nEk+up/DY9Ry84WqeOus6zDggXWTM2Q1YNZE7fwsFH+yOduLJ\ndlLWoBSocpZqUZF30khRobqll82pLF7j05jxUS6aN4MfHHUxOtjLA0+18Y2NAZOG+yg7lC/LAd+x\nLlIoYqoGlnFMlBCHuHSgvg5PK2RJMeaCh+h3Vwf57l2IhsPZlINYwqUUCJJeDI2PEklmTp3Hixu+\n3nr8h0gKfTr71HwQKgeq3NbBBIAT4hhDyVRxjEI5PlmhsU6NR1+yEFPQIwzKCREeKBOgolECUSEq\nwppewYsgdBnHxIhZQeHfbmXWGXXMv3MiRy97lZiXpYRDMiqZNx66ItcyZf0W3r9yD5syV7PwF4cy\n967RzFcPMOnsuby2uh8vvT6DSd/bxrPLG9iSLaC1xhiXqghQoQUhOFIJ/HKKH7GLd3Yezo1XBLz4\n1dEc85dWtFOgKxjPkYfOZdqyKzHvu5w683HGkWR74BMEmnwmTyoqqU5MEmiDQeKpBMcMaiCdShEI\nePe1NoqdRbQx7PE140+Lc9b3YBwl5t94LXfMHsvAS5pRspu+vmWz9Cj4Pl2+oafgUygGdLUbVm3M\nsLUNho0N8NIDiMvh5HOdbF7fTl+VYFcugIRFVSwv/G4y7bm7+eSTPoRE2Z29htWZY3jqNxO5dFIb\nVQIqRYcevw7fj7DVFLjjtQKrF65md3eSG99oZ8WLARfP8dnw3HRShQtoHH0lQkh2tb/Izdet5LGc\noTHdj1GDxpDpXMa4CUnGDzIUwifwEi5+eAH9O5ehu0ex5akOms77CSnTDzG6mTDWhC2XMJ07IR7B\nwRJzC5Szx7N1/TQya++h8bxhBEbzwK/OZ2dnnOX/egYvrPgFfHEkM157Dpyv8GQRazYSG7GG4759\nDEOWPs2k8+ZxzBF/4cxFl4GrEFaiGpLER49Gt99I4Y1HiMkDZDMN5aBAvlNTCAqILS6JdJpYKs24\n5jRHHpdmz2MBXTnD609meOvtFJFSNwenEqhIzR0MK1AqxHVDAhOSrAhQikCGxOrryFjFdd+7koFX\nvMZL2RRNq+/GOMOZNHEiyxZdT9IYREWyq0197fX4D5EUHLEfoiGiZqRbG4SyqgbYEg46NMQ0mNrs\nJEJKHCGISNCBRQG+DLChwjqWiDG4UoJjCJWlWjQI6RFIg4vm4Z9tZnST5MG31vGvw9exZe1AZEOF\nvAnp6oW/vDqUIx7Q7NrZxicyz3tXT2PS2Dt54vKV9ASa1a/fzBu/fY8n7prI4EEFNusEtpgjIiOU\nnBolWNgQ6yUoH6K5r+MLnvnlNMrcy/GxEs9+1op7ZJyuXZoh4+fz3Bc/58QPXC6b71LI5PADTSmr\n6VhfYeSjDaRxWVPpRQ5wSbakcZJxwuQATLKXYy6YwMPXryaf7SQbFPD2fE7sjKl09HqsyrWzBZfr\n7u7kzWyFpnSSLdYgtU+5CKVuya6Cw6ZsJ1vWwr6KIKbq6CrkaEolwItiqiFvBxWI1qwvr7kjzg0z\nr+TTQ0eh3JewokD/dJLnXnqEi+94gFWbd2IDSeDXIWRNIRlPKwZGQ1aU6ti3opPnlo4mtSSJbK4j\nGR2GDT6isGMcHTsK7Hzzck6Z3MvW7z9B6F9O/4Y0oyoXM/h3LYycJ8k/eQnl1tU4g+6mPOBOliwf\nSbkhJDVoMonm4djmCLKYpSeXxf/nHRTaXyN92uX0nzqLcS88yJabEoQz7iZUIXN+PJ5Va9cy6oGP\n+PTIzaSaFyNlEvAg9LAD5iCKT+EWAv50+PmMPetsPimu5Im14/hKWnBB+RodFimFPrG0IB4MwKNI\nPOESTSfpm2ig6sUxYRVDhbDXpyfrQKFCvRJgJBEqzHmwnR9km0kMCPg08JFCcqQtIlUzjhvFCINX\nY2KBFHihQONQDgxzTn6cq89JMPfDLpYMSqCSzTQ1+Bz68nqmPVNmz84Ka3bk+f8M1/9+fB2Tlf+M\n+/BLYDqw78Blt+7fv3/DgeduAa4AvgR+tn///o3/1Xt8U/Qh7kollOokAAAgAElEQVSqUmMqgsBx\nqJEhJcYahBFU1QH/RVlzZj/gx4qDoCwtykYJylWsgHIYEiME6eBUHIRTQUpQSoK2OH+6nn19v2LB\nh2s4e/bHPPZoMxf+OIBEJ6nr20he+gi33HU7L8/ty6lTFnHvYknHU4Nwz7mDD19ahTIzEepiJk29\nkytOGcXIe9fSM8gjWzCUVAN7TEBQsqSUx+wpC9m85Fg+PGQWX31wIh0Lb0EHHl5HQFOT5OMvfKYc\nEWNVxUNqzb62Ku/nDGv8HGpsM6f/tkZTemfAIE66vJm+yQbqR7cAHuWcooeAntHNbFh7Lw/+ejLT\nv/8pD9th/HRuhQdezHDODUXKZQcKvVy7uo3GOmrj0zYkdCWi6NI/maZ+qsOu7iLlkiARKjKZXqSK\nEk+69EWyr7fInXMuoOf9EcT2KvbtH4wcfgKCEGvzmOB6lPaIWYfUsQ2Mn3cJPx6b5Mukw75PYuzu\nzbIn24noeY3FlzhMmzqEZxesZEvrR5x/bhNJY3jlqzG0Zsbx2qJvctTRs3ly6U6GjB6OsgrZnCWV\nmsIGfzCF7A2MyWq6Jj7P9pUuk+YXQGpMbgG68zLq3cnEh95M7Kp1tBQ1qRdWIdzh5Bf42LVjUdKw\n9Y1uWjZmWBCexguvS86fvJPSxu9Q0L9F9yrSg6YSa54PPM6qlY8z5eQRTB68ijE/eZczJn7OGgSq\nGOBbQ7FiqOoKMZlg+oyfcMaoZUQHpIn1rEC6LsJx0CagbCT+XsMHpU7y3Yar7hjLHVe9yIa13ezu\n1tz7ZJb3f+8SVgOIhPjK4qo8oYijDAgp8RUkLRAayh7ENfQUc6y6JsOfCwFtb2fZOsfn++1f8Xzn\nFv70QZF75k5g+tQWnv9aKeG/z30AeGD//v0L/uOJPn36HAdcABwPxIHf9enT55j9+/d/+ffvQuDG\nIfAVCEkSe2BwCbQQGLd2o5YKwnqEwtQqBAZCp+Y3ELFQUgaHEKtDetAo46OswLhlkDG8ikU7kimL\n36Dvvq189PFmXhj7MdOvXM30246mY/FiXtt2Pttfn8bYMzM8ec/JXH3OZ7z5occeR/LYHRP5xYAC\nSe5Fm5Xku0u8lFjEI89ewvHnXUz5i368uWwNixe1sKm1xIZCEj9Yw6+m9Kea/JSdLS2oc5oJ0gN4\nZFaJMbMU6369lg3tKeIywab8RtpyWTIbd1AILOvWTWDcsZJycjSn1J9L/3SAiSbwVBIdaKSUEDX4\nLpw/azJ1W26n50vLrNaxvLKxnYcLDiqUeJ5kd85hQ/tyFhdWk1IJ0v1cLp48mqbmvVw8wUNrQSaX\nYs9On+PTUZqGKiZJF2NDtO/g9ibZtDPPy0uzeF8O4RuRTtyxn6AdHxPsYF9xLsXLT+axecO57peX\nsG5dmovPA92bp1Iqss9qsJJPezNE7rwXsfByhp02gAvHHcPFmbe45sYTaBu6k4E/uoyBiRFccFYP\nQ47ZjZIgpWXr3hfRThzrS9zKzZRVJ35dSKplJLGb1uHn1mMLBqRLqvk83JhExVyU51OVNdSgN2A4\nwYMjGXZelGveWYFwhyKJMXP+kbjpTva0nsBJx/0O/zur+UFLIz+b9iyFzhkcG1/FQ+s6yWNwjEMb\nFXxzGcWyQzWU2EDjG0Ms4fLTGyZyzYw01o2CCtAOECp8UmiriacNpwYKPVpS/PNE/unILL84YQLz\npq3lwuvamPOI4LHbE5hqDhv3CMqCqiyDq6hKg2cPqEodh0hF4sgAiUTYneSLWfYUJCWbBNeQlAHJ\nOg2hoH86/fUyAv9N7sPfibOA5w8YuH7Yp0+fHDAI+LtWsl0HH0zEcUm5Fl0BXzpUgxo8VGgH7wBN\nWgGEZcIwBCeKpUpoa61KLU2NnQAcYHwTYgkQEDgoUaBEERl1oNUwsHMco7+cx+X3Zukvr0faZj6p\nnserDQn+tf1szmyG3/3Luyx88BjmzP0+jZ3XER/r0vNgG0KtxKLBwmWzBmFkH0i+Rdgske7jjPnT\n9/nw4330Ty5hWevjbBcPsSCynSGXzmL++ftpPP9LDs/5XOx1c+Pz66lPe+zOrOfZX73IrcEOXsl2\nsmRHCW0WcdmD3exeMZRSwiNjo/SXEqs02hiUEnhFh7iSpIYmkB+MZcOOCps6s2ir6StAOC7S83jb\nLZCqc4hH01gCnl7fSjq3lifnpGn0fLpyJZQHt96R5qyzy8ikRJgQowU9bo3imd8b8K0frqftt6NZ\n0v4i01dOJgwPRyfOYfMzT/OHgs+jt08m5jmIgo8lJEIvodHEgUA6DHQFHprFizZir6xDngmzfnwq\nwwYkeXHbcuZd5/DmvzzBR+8/zWNDZzB9/kTuOfNOluUlc1+fweCWkKW31lNIN5C0Po3Dx5OMS0o7\nMqj0WFI/upnUxOE4eJApYMxOjPGpmBj1Q0cw/dluCqt/Rs+TtxHpdz62eyNbV79FX/kX9hw0mG3/\ndDdvf9RJ65sFjrz8OcY0u+zqbqNMEcc35IVGVLNgEmAsJgwoaEupKJg74x62HjQU6TpokcNY8Iys\nbSfDEtIajBGgIQwzDEmm+NWc4fz0mp8z5KNTWbMyR/x7zzDv6Ru4K55GUUBQBBPF+AarFBEJRmiE\ngLJjEJEQEThEhcZ3FS2kcatV1nS3ktEFjPJwog4EX9+O7f+kpvDTPn36TKHm1HzD/v37y0CCGhzm\n3+MvB879b/EfuQ8HfStO1FOERQ0uuBpMNMSGkhCLY2sKMIlDaGtj1eZA59VBYcIAkDimxuMzTogU\nktCxaCHwtKVqLPuERWkf5aTYVckTasULDGXR9K2cP+tx3vvrbua6j/O7+Rb3u5uY3fIcFw94gvm/\nWMDml4s0zW8iMlGgg+EkB01AJMaTbx/E9pcvI8j38OvgEIaNvplJN3/KE8FCfvL0M5RtmnL0Suo/\n3I20G1HJlRSzP8c7cyAdbpJ6opRKBV56roOOje2U9hbZ1ampEueyWS2oqzxWvNKGbh6Kd+6omlaj\nLJA2JHQcIgredSVr2orYHTm6kIhAIoWDrEsSCwNkzBLvEJzVkqAQeOzJGqSCfCHLyMd3MvvKZuqN\nYuCPPMrKEHNjuF4/6l0B0rK9dS8ihMAokoFPcMZT9PlqF0v0TB41DvlckVPuXMGe5dsIigGmMyCV\n9lDSIoxGyQp5QJoAZB3xqIOvC6x4ejkXnXsJD3StZFr7TkZ1N3L9W6N59q05jEnPYdbJmn9p7yI9\n1IWKpbi6lbsu7WDxUU2c+fp9qKShq3Mnvt1Iy8QQ5bi4qgWMQ+j7QEDgt1HaNpfkgAby247DBovY\n034q91/wMJu6VmDSkLkqS1Pye1R7l3NXwSc22OekX2UZcuZvGNZPsaV7NloXauwxaagajalYtFXo\n0KDzdXipRs65+U3OHCtJ0BdFHbbSgV/10SZLuWLxrcaYveiCQ8kUKIVZlCNQ5xsOVRJEFFMsoOru\n5Zzzb2brirG4KkNoJGUnrDmJGUsAVG0NYGSRaBkSSo9TByT51Ag6WjeSMaDicUhKhgw14PzP6xSW\nAHOB/Qf+3k8NCvO14z9yH5zvHrS/r+PSE7VIY2ouy0qitEEQrfEZhMRaqDUgyzUL99CpcaitwKBx\nHENoHEBhQ400GgeFdsAPa/oBX7sEoY90ICZ8TrxuI3HzMbxwDV/8fjfzl99OwgTcvBIufEQyZtH1\nTB18PTePPpY1e58h2TyZSjlDZuoYfBny/OrZdG1LIYOR5LmBwoAx/PGfRnHeTxbxwhF7sM6LDEmN\n5cwrH+G2B2YwaWo3R301HdcKjCnSVVTYnMPh/eL8OvgNW7N5MkSYecsEdv3+Bs7I5nlh/SMMbjEM\nUUlUREFV1wC7wlCyBmUlBJr57c14yQKO46GUR1xXsBFQCcHhruTNAUlMp6bk1ohUgRI8u3ol0ya3\nc+usFI0NcTYVFamkRQmF8gJkTJLKSDLFAikPegILVhEURrK3B26dNYGlb2SZt3wlKgRjK5SMRFV8\nFC7WVjChxgkD6gGrwIYu8aoA4+OQ4MFl8+lSu0kd/S7yu0czauwTZNrb6SiUmD1jPFeccxG717Yx\nZuVrBLqNNmO4JrMd63nEG4ag6zpx27eAGU7X+ivxkhdB1CMSlti9/kS2zD6EXZ/+goUL7mRFZCs/\nWncQR+58ns3dWwikoBxV6PYsYahYs75EyTdoG2dgQzOlHMRVC1HlEnGTODKgI7uTV/auxzGaoAqI\nkJk3TuTqmzvZ0L4Dz30FL9pAWAmIFrupmCzVaq320OP7fBoYuoo+Ol8grMK8RwqccepJSFnzFCr1\nljj4iGvY9PF5vPPSVDbcUyESBhi/BFIgcTDGoEKBrnqYMEo83QxhL10dW+gqGKSExn4D0NTMYU34\nP9x92L9/f++/P+7Tp88TwPoDh0XgyP9w6REHzv3dOOhgQTXmIkMQFYN2LAqNjYHSIdoeGNqRhoi2\nNSKUjeI4FWxokCIBjo9A1DoORhA6Gm0USIjYmsmm1j5SgDTgONQm35SkqgAM3/nhMdx8SzOXf3ch\n4zbvwzl0OE0n7+bMN3/LviPq+f606aTOb6GnuJPSpy9x+LfhjGnDuPSZszjem4gutHP6t65n6s3r\nyf/zIqq6H76pstMYdmVD5u9aiVjSDqKJeDqB6xfJ7ylQ2ZHgg6zgloLA/6OhqbmReGhwC6/x2IIs\nalYj9UNSiAFlcBzcckjgFDGhRFJrf1kLr27v5IrxKQA8AW4EjBAIJUnVu/R/RbIlLDKkoYFYtJN8\nxWC0w+LHX+OTj6sYap+/bwy+9nGdBDLvUi5pyrqMDQNUAirGgg+xo69n1oKldFUCtC3iuxZTkbhC\nEEViQwMYcGqgHRNzQVowFRwvQVjRLNk9Gx1xqE8Nx37qkim0kOp/PV7VMGTYaLZ27uSe9U+QoZM1\npsKZk4cz/MIikW2tqNET8DwPDOSLBcJoG8Y8Q5c/lMrL3aw66lROWXAYHXV/5ZvfP5z5hdvYvkdy\nxahGNrRdhkwnkVWgYpACuoo+B8yTSMlmLvkhPPno28QHreMLksQPi/LtlOLzrs9o/vh77PzWn3AM\njJrcTEvzWN6NrAersGIjQls8leJQV+IGBbTNUgoMJa3Zkq9QypXp2WjwtWDJoo088N0dNSIQCpwA\n6TgsfmQ9Ql6EGjeHW6cmOWf8AOIU8CgiQlGb6zE+gih4gnxXhp3bKghXMXBQAgyUpEchqFCN/g8P\nRPXp0+fw/fv3f3Tg8Gwgc+DxOuC5Pn36LKRWaPwesOO/fsWDiTgeCp8wGmArAZoIVSGIWIEXVtHU\nxqC1tCjjoWWADZ1aL9dorBEYYalaF0KfCC4RK9DSJ0SSTBp+d8u1XPWrZxh43nB2pwPyuRL5YnCg\nmFVBOZJjLmznwgsHcMWU4cx8ZDgfjrmLknsXrz3zb/RkN3JMx2y23CDYtLeOpbvW09SUZlgSljZ0\nsvT+brLzV7MsW+RLIZm3fDqJhseYueSXTDnzSnSiwo+Hj+JipwFXBWSihr4DJSJbYnBeIj4pEokm\n2ZM32I0+L70j8a/yGXnuIJpTA1A2jglBGoNBYU2pVj7RAbEwxO+t+U4IDB4OxqFWifUNqajH0c1N\n3LrtaSKJkPHjm0muzbApBk4ZPClqYijXoacQgHHQRuIIyGQCdEVhdEhoaqg9zy3yyhs+b/6+gvHa\niAIHuxJJBc9zCMqNUC4SkxJhDNZIpLSIqgKl0DoHDsSThs1vZNj+G0n8DY+TThvK220FTugdyp9f\nvZvLHnuaTd1tjBnRiO4OePaPa4k3SCLakMqFNKpmtrS24QbtpIZPZNrDU1Hx4Ywor+PRL1s5+eq5\nrHHOwseSJ43b37ChrAmE4XDt0kMFYSRl7WKQuNKiHXhw3jzeeupdZj7wEvEBU9AyDmEGJ6iw8KQs\nv1u2kNHzPKrJfhzdHKdt5060H1I1msBo3AqkEs282aKI2wJlXeDtgqAYaPbsqKDzFj902d5e4MjH\nVuLnal6kUgqEBCMFUhmE0Xx12PX86RtjeT3YyskDNH5YQAZFhJ/FQRMQQWnDu4FlVRBFuRJEjJSV\niDBP/0aPYcn/dBf/n8Z/l/twcp8+fZqobR/2AlcB7N+/f3efPn1eAN6j1kGc8V92HoCDDhJIvgWO\nC8LHyBrqzQsVRpYxIkLEqaKNQNmaK7OsSGreugpsQAQHaQ0yDLGEGBsghEIaDyEFjcph0e0DeCq9\ngS+e6sff8ofxYXsDs791N6lZv2HFG1l2tlVwQp+bbw0oFVu5cPJF/PCo83h+VyP7whl0dc3GD+Pc\ntVEzMJ5iSKGZ/CuwVIUstt1cd3KG53dA6ZZmTh1u6co8BamJnHRkkqV7HsccL1COJO9KnIokcCqo\nWAM9iRj7EgUW3jmVxbe2Mn2Cy03zJ3D0RIUiRf/hY1EJj7h10NUKJRlCoAl7BSrQ2BAMVfQBuGtM\nghA1fqZjJGHFUnUkqfp+DPlNms5AM7hfgo5kgPLbqTpuTcdBiZQDu3KGjkIvqWQO14HM3gCJg5US\nVwocR+EJhbGGrVu6QPkMbDb0CJCeoS8u/c3OWgdJgWejCEcijMRGKlC1GL+E9iVeZACHbOnkLV9x\n6ugG3m3NsarT5bYvl3DrlP48sfJFEg0JSAwg6O1ElA2DG9J0tXci685l2rzPWdy8gKaGJItXPoWY\nvJLnDvmAVxfcy78NWMaG1z6HUFPAQ8kyRsUQSqKMRYc+Me1SMhZrQuqx6EQLMwddyUs/38PQn5yO\nDq5k0yPrWbb8cfxSgQRw1SkLOPSTZkYkEhydsHzQnaGoJVW/lyC0WAzSuhTKSZq2SErtBUIbUi5A\nUNGgNRYXXRSsWN7Os48HCOGAFYTW1DyKtYEQnKgH0YBJ57XwlbwWKaPEk41gmqjqZnRnDr+UpYSl\n2h0H39AThPTFxZcOJT/OzClDiSX/LxYa9+/fP/k/Of3k37n+HuCer30HwFf7QQlFWYQgI8gD5s3W\nQihiCHQNHhtKqjZAEsG6VaypHUdEFEyFQBus4xNBkFKw8pkLOeeEkL8UA14TPr0/fYpXnp3I9iU/\nwYvHGZdq4KyXFHfmF7L83qmk9H6mXzqP5277JRec9RmDXv0zL736PmP6GcZMPpf6VtBeGc+x5F/0\neeWUbsY01DFyguCulyW6qPjAWF46twE/W8DmDbYwggc2trNnW0Bsk4MThtTbGl9CJZMMHD+RJpVG\nB3lkg6apuY6BU4dw/Og0jUkPbJGkYwkR+BiEiRIWS7XaS9mnqkNiGYvVIBqSBBUXiaUkFMqANSEl\nDKJUe++jMylMt6FLa1KOSzqZRhjJW+9oomnIqxDrldizvkC+M08ylUREJYGp+VNIz8VzJTooYoiy\ns9jJ1tU5xkxWTKoCEXi/R5Lq63CcK3BDiEmLQiGcXrBgrEM8LiEw5LVPWjdz05jJDJkjUS+G9Lz7\nAPfeHGPh1ffR4XczfPS5jJl4GwMXj6ZnR5ExiecpXfQT8sUk/e5bTenDg3jzs2PYfHgrv/tyLd98\n/s+MumAJq26dDYd8QL38Dbfe+BDL1m5hq78AIaJQNlgnQmirVENDxFTwtWTVilV8on/PZRdczajR\nEygFoORQvAmnQ8rwSWj59pqn2Hn5UzTuv5Yz751OVRuCELQtgZFIJRCOwfRqNAI/LGB1GR0awiDE\ncT0CK1n6WicLB29ECImxNXxgGBrC0MFxBCGgQ41yJJMmJqksyyIrI8DTSGlwZZJgaJzIQA9dtpzc\nO5+3e3L05CShNuiES5c1aBPlkFj4tdfjP4Tz0n76YIxAYVG4SFwgWpOKWlODbFQiOIRElKy5Xdga\nOl4RxXFAOA5IByFAComSitcfrfBJ/nTm3PgIN164nvHTvsNLfaMcdZPHjx+PUo5ezEPt4KkUM2cu\nYVJzjmsv+Ca73l7DiJPKvP7SZp69aTCpP/wcP3M3t9yu0CaAQCCNosePcuMjRa57sMi6jQF7/ICH\nlw4niaArH7An57Orq5sNq3cSao02UMwUmHXpRm46PcO61p1o3yefzdCR6SS/LUeLGyN5bB3JVCPK\nSlTERSiBsmViFUM1KELFUDFFyiYgMAZdCTHGsKfg8eSmItkCBH6AryX5qiXYZ/Erml0Fn7e7S6zp\n9Jm/OktHziciE2gL27PdGB0QaoeIoxjSXEe1UqtVaCNAWTzXJe44KByUgrgU7NmRI1vI89Btr1Fo\nr+K5kmMOgUMxOPLfbfkNEaeEFJaUhJSUpD1IJQ27WwPq+4MXldx55SV8tOtZhiUFp5xzFms2riSd\ndHl4/uW8f/Bm3jn0ZF74y7s4bhpUyKrH7qO0bTlx1Y1tu4xJV1zCt37yOcd8cyWHrvmEab9eykdN\nj7Lt4xEctvBjznv+cB5d9QNsJklPpYKvHWwIEQNOWEdMKM5KJJl36TBuuredjmIzyEZ0dhtC+QR/\nWMspB73H2ukuI768EMrPsK/YSzEwGB3imhAhQUqJJyRKCgjLOLoCYQWJQEqXkDo6tuVYs3onFg/r\ngAgFmtrnXYUDjuQ1ZayUDqkGWSOZk8URAZYiwuRwTZ60I3GFxItK0t4AhqWTtKSbSao0F0+cwB4x\nBFn9+oXGf4ik0Oerg6gal1ArfGswoQQniSGKFRakREkH6UgwLrZWYUBFLbWh9NrreLgoJNL6ZLo1\nx31/GcrdyPVTdvLyUsF9l13LQc+vovX2EitfeZa/DXyQN/4yiD5ffIM7f3YaMZnmqotO5eLmTja0\nh1x03BcEhUfIaJ9dxU56KhrSsKzYxQ8HDWXcjS0Mn+Gys9shX/RZ/IZm4GiPk0cPYExjC2EQ0NFd\nwC/3Ug5yZN6AdONkYolzidFIIavY+lobv336GbY81c7WbImwkOVoTxJTDlYEtf0hQBhS1UWMCbDV\nAKtNjYPp17gE5d4C83/xS844ZRHXLWjlnULAe4UC7wcBHwQB72T3sieneaK9yPOdWXzjQxScOk0Y\nGhbP7cLPgeoVOLrGQBh3WiN9EwIZajwktbk9gaC2RQAXv6DZl+vmg+4cPz5jPeteM/R3k/RvUPT3\noqTqGuhbJ3ETgnhdAlwX6QoEHuWyS/3EFLYnpKklgUo4kEjydraTsGM1UuSIJzyMeQWbW4kqbMQW\nZzPjjLPwgwLb2+/ljG89SXHJSayd9Dc+v/8uLrpxCZc8XyXI/huFzuW88dVH/OrNdXixNGPu6OKo\nu3dw6zpL6EeJBSFf6hTW3MCFk7cz/IRTaBg2Atwkn+3+E2PrB/GXj44n4/kEpXbkoLFMmrOZ8+8Y\nzNLVq3i4/Vwifk1iG8VifIeEkMSxGEeCtJRtDoVbs6ZPJChLjzVtRRY/lamR02VARNTMaURIreWo\nazqUEIvB4gcVnMDiVTSh3Uus5OPpAKXzCHziaGLSUG80R8Ykh6YSHNmYIJ6UNCUgPuAq3gr+L9YU\n/l9EuP9gDA4aBcYFE2Ctri1w4QFgLNjQ4giLdDy0BiEECodQCCJWUhIGawW2IvD6wfWtKd5/8HCK\n4UgufOQd+u4OODp/Kpx8G/2/4bF922/5YtMTPL5vIssXX8O8Hy/AVqoU+jVS3ziUvnItHRufZlXu\nfvYUeuna0wFFHxEIHlvfxsjmNJvas2z1A4pZl32JkHWr8rxpipw+oxnRXuCjAtxcLLNlm2H4iCKn\nnJsAOYPSe728093G7m0FIm6ch5+/nDj9mLS0nb5DhzOsCvGIQkb7IbTAYgiNxQYCU7GUjcH6hi8D\nyOgQXd5IuWAwxnCp/wxTLhvOxVMKxIWhLAV+rkzEKtZ070Q6IViLxSMUGuoqrOrM8+AD3YwcnqTk\na3yjkBXAgRghVUBUNIYQFbpUbUghV+TtTCehNlRErW2ZlM9w3fmvcf2Nac4ZnyRlJT2BRh4YWkMF\nWOqABEK6nDUhznbyxKNZNq0MUEPHU+zdCSaDlA6l0Oec02/jDwmPL26fzJIT05y9cC7WcwmdHKef\n0sSKbx3Nj947ks+aD2Jy/8HUPfhz7kwYEmY69cklrFl9B3+5/Qhe/eplbgszfHfwx7Q0jGd7toGf\n/uzPvPvXCdy3/TtMGvdrZhNw7Jsn8+KnmjXLv0929VdcevHr3D/4Ld78a5qk6uazL85j6kNbuXHm\nSB6cdSN3PLeIKoKIkEQQCN8Qc0OSuCiVIJ5KorVke7dgxdpONrV2UigC0QjShBhpUVbUfgmIGtRI\nWGpIA1XbAnbsLeJEE/gVTUlopLHERQzHQimsYI3FxhX9ywLrd+NbjShKeqoaSRoT8b72evyHSAoH\nfdUHYSOImk4QbSWSgDBUOGHtJygohGMxgDEhKqLRVVWbf6gpOVFG1OoOjoNfqXDRhH9j+GdNnHbk\nI3zjDzfz2YStlNQOvn/E1Xzx5Qm8cl8Kb8rPmXRlGnnsUOqVQuYKjBua4LIROVZN+yWnPjibTNYn\n41uMVigbkpSC0CrWdWfpKhbxHcX2YoUxyTR33NtKU8s2bsquZeuiTqYEOXZ19xKhTONdDi8//hQn\njhgFso4gp/nxlBSX3TOAi+8oE5oy6z5rZvYqhTlXYTMh2oQIYjihYl9FU7EltAkxVQdbsJigil/Q\nmAqk0nGSXozAl+Qf2YGV7fRPJUgmksQcuGBqC6P6KQg1+aTBmBC/ABBFSsuMq7fw3KNDUPFo7ctm\nKkgbrf2T/FrSNTlNImprI+qA2Otjog7GCSkjCJXkAd3NqmeyjGx5kduuGs7poyWvZH3+ttdw5LEe\nF4xK43kxmlqG4Ad5YnFD2cvSUSxSb0K6suuB62sj5FLj64DtOZgn29ly0228Ij1GTZSkQo2wPh2Z\ngJIN6DGtvDppNOWtcTa17+Uvd5/CYWcNZsWX+7ly8xs8c/ZSZiyYwMgJb3LSQVt478HRfLrs90y/\n52RuW/wWyYYMYu85oBfQ4mnEnNGIxHimfvge/ufXcIqqcFB2NHWVmzl/4FQGBg18ekQrVb6JSgjq\nGwJ8kUAaD4lD2UliZQOZbCdrWn22t+XYkitRDgVWVlC+Baq8b8kAACAASURBVFys1FQAxziAQIYW\nwgOO5Lo2Mj//sTbC71yClAXQklgiiTQWIysQlBFCIP2AjG7noyBPpaDZ7ENbIUep2EC6IcbXXe7/\nEEnhK/pgiVIOa/h0JAQVC6bmoIsJKFuHiAhxsAgkxnoYdG04StgaOEZIquiaotG1vKRDgnAYt154\nP80X386XX75Gae57fHbHp/xOT+Sro5ay6ol2+u4vcP+qgJdWzWZL+yCG9BtLT3s3644y7DQ+ZbsX\nNMSESwwPIS1GwxPLO9m6vkoxp8l2Gq4eEcUaj10v+uS7V7K00sy0qc3MfnM0JSuZ156hVPF5oZqh\nbUeOZaIddXOVc7ZcRNWUavvReIXBCZcTGicwafRE+ldayLeW8Y2goiuAwVRDtG8IAk2gDfkgwDcQ\ncy0RJBHPpdF3KVU0uzMQyiJNLS5nSYfjUymqVhKnzPZiJ2UbUiJKfVKxq+gz/+EMs28cTk3eVCGi\nDfsUiGpYk1W7CXa1dhJKQcugQaRGpDEmQ1wptBMliQuJEKSlRwtuuqGZmZ5hQ2czPNGODWF7Z5mU\nNdRf64D2sck42zMZzrko4NeP7sSIjUht0GGBwFxJYHxM4JMaPZlhZ6d5+41uSlWJH/VxpaSwI48b\nRqkHLnhlO5fco1H3J/nusOP564qpzH9sB84v21jTOo3+75zLydvLjJpxGps//oy5k2fgpZvpyed4\nfkErl42+k2S/WTjJIqawiGrnZbygVlEqKb7XM4dTKidx4jk+mfJ7jPvNd5my/USaou21WhcJElKg\no6Argi3/3M3ugqZQ8LmomKNkBdZVRHoNiihltybNN1WDMWClQIeWiAmpOge6BaFEyJAlj21kQodH\n/PICQmjcigbXEvPhAxXiBoIAH1PNUMwW2J0tktVQ7rXszuYoB1/fju0fpKbQh7KRYKO4pkZqcixI\nB6yVCNuAsApbjRLaA96C1oIIMI4mYhURR6HwqSekPpSIqCIwAQObU1xy3ATOv6VKoeV0xOQjiDRc\nQmNKoAY04KN49pU7GX+iw63jh+LVBUwaO55VjywkG3SS1z7aWEpG4mhJXymRPsy7upNliwxB1mP3\nNs34Zmh2DV0bi0T+6DIz7bLimSxVbSgnksy6ZwapI15h99aH+PElo3G9KiOP9Xi2bQG7sxspBjms\nqRAXFkKftzKreWD1NWwq3oY7ei2WAtomKPkeRksCA/t0L2U/oPS/qHv3OKmqM+/3i7PWvLUmWTWp\nnbBbq5IuYnWkDN1jd6RRerSNoBCBCFGMEoMXRNGoY7zEEOMl3iOaeMH7hRhR8T6ogILBS2MahyZ2\nm26SwnQZikxV3t5kdmVqOWeVs1bs94/NzCfnPTlnPDPvH5n1V332rvrs+uyq59nreZ7fpVrGS49W\nGh1IgpygEGom50ImFzJ0ZjVfn9eN0FCr1aiUIpTLgxcIkSRbFUhaQ8drfaM8eNcA28sldDO9t0a2\ntApLVIoZ6q8QSU1W5YiGRzm8dzaHd88lkJJubcmEgjCE1ozGhYrBnYJsm+b8RSG3XtfDnLYW7EAD\n0a34zv2D5NsyTM1PQ4gAEUJDj1GLYxouRnpBLapgbB9134cp9SOzDpH2RFVHWmSp7IqQskojdnSH\nWfz286k9fh2CCnN7u1mxTqDzOc5YMo0f39TDQze1cMyxgr8K32bX+xcxVMmDb0fn29FZw851qzH6\nJAyX89XPn8xf71nKF//wJS5peYlyPzBpNjmv0Rru6byfzsv6EUqR9yFhOkRIRaYhGRmO2DJQo1o2\nKK9obdFoLVFCQFqitUJrQDdQKYFOC5ROJ8Q/lfigKqVQEpoKojjm4C/cDUEPARlQEULGeKXIGomV\nhnp6GBoR9TgmpQM6Cx1Mn9TO0e1tSVn+EdefRVLoBIR1uL3EJ4kCAqwLcFiauATlbcEYh7cBItYQ\ntWFcYqJRx4IMibSElpBMNuDdl5az7ugP+IcTHmDBQffw2dRjzP9SBeKr6LtsF9/Nn89f35vnxiMk\nO0vryE+Lef0fnuSGy6ZSGlxHFDnqscF4i/WCss0g1Ww2/b3i0buqNBFgqxR6ssyZ1sa+XxvF24jb\nv9RgxWbDI4u6YZfiqzrgpWc2cuX6+3nLj/CecYS5bnp7OogGY7IiTYBkomhS8BolA6boFrSDnwxu\nZMPgKrLThpHK4HE0rcJHjnocsHOsSmwLTNjyJN3FLgoiIKsCdNqj0oIwneakczs4Z1EeH8VIbVGB\ngqYgtAE+sqhAogQUii3M6mjj+XX9bHl8gNKuKhVvMUJjUPR+qYvHX78e+ckneesvV3D+rQsILczt\nyDO50EtKZ9AaMiognBTQmdecc+16pp5Z4dGNFiblkSrDtTcVmZ7NcXx3nkd/GRAElvauLE891kFd\ne+q+TlMKnFJElT5kVMWxi5Goj3I5wtoxdrpXcbrOk88PsmFbRNe0Nl788TxaJ1Y44F8mo6rb6Ewp\npuaKzJi5iLvuuZinn1tMe0/I9EsWcA0xP3zzW1z33dfZ8PRmvvjJWdx8e5Whp+fw/ME/Bhlw+NWH\ncdDJq5l2SpZPLjqOK165kc6ZSxAzl0CQKIRldI68SydqS86TTSkqMWyvVFEa0hmF0holFKFXhDJN\nQWfIKU2IoDUI0IFMuCoSMmmNxyMAh/s3ejBIzyVXreHwoyuQ7iJSKXyjCmIXjgah07hGhbqFg4tF\npnflyKctrRlDIbB0tv03E1l524MnBgfWB6AitNVYDNYn6h4KTV1YtBeAwohEG1DHCpQk8lUmffot\ncjk4pNjGrEk9zH/tCgo9MV9v5DC/f5s3vvs8YfA+lo/x8Dc/oKqK3PDuMH97yyomtx3BpyoL2Np3\nNPc8cjdDpYg91hA5l/ge1Fow8Rg7qxEH3FXljIE08egoj5YMt1zSxlfP24zOhcwpSj5/VB99P1nC\nQX2vcsgJludWj1DozfNk6Q3s1mlM1+1csXwuj9kip66xBAGgMgQKPqHTBClNXiQ/zacQqDiiptYi\nwh52rFfIKCBuGHZXIhpxxL0rF/DyZ6YTHySJI0EIKKUxwxHtvUVuPq+XWqOKdRHZXDu2KpP+AI5Q\nJ6MugWN/FUI6zYb+HFtLloefHqC9Jc85ty7iwstnM+eDgK078xzb007ZRJz8jQZvbezih1etprOj\nyO7IQaNCKqcIVcjkIA2BY+vgKEd+twYqYOIqwX435ThU1Ti4GHLVWdPICIelhUzoaa9EWGuxYYHy\nSEw0WqF3moGUp9aw2KgP0xijWVb4cCav9I1ynl/N+Ytm8hntsD/YyqWVy/jqOS1M7V3A8ct9Anhr\nwIbNMDUds3QBnPOh5dxHBmAEgmv/hguuL3HEJ/8Hj37HcWbvFNzLR3GpWIQTDd5f83FOueB0bl33\nd+j4ESYX2wi7FiG2rQEPIp1KcPMIdtY8T5YiIisSFTHpaFqPFTHep6EhEvStMrggwBiLTouEPYnC\nOYfW4HwiDYAUCXzfNfFS8a8fnMZx52znhWe7ENEgwViFqktKEOln0pozeIYZGhxhaBhqsaNchazv\n+Mjx+GeRFDo//BBrmglgSYCzKXAW4RVSWHAJei7lBM291vTeNxAiAWTUooif/mo2pxx3HteefjWX\nt21h+7YDOXZlnfD1dcw5ZSaf/2I/Kx54m9dX343C8bGBk2g58vdc8LN5iOBcvrt0B2dOy/LgbTdR\nq/lEKEVY8ApEllQQ0RyVbNpWwe6bxthdbB+tMGthN/c1xthpPOmb05x72SiqJyC8qx+7YDot7y7g\nM2ds5PZ1t/OrnypCEVEL2tG6g8PzAV8/4gS2llYRqIBsOgGthF6hpUI4iZAKpRxRHDG5WGHHaBVX\nUcigja2jlqVX9zL7hbN4eHAzH/8gJsgIst158kGRR/sGOLo7x/4FzSuVNN5uoxzXEZGgNS/wqkkZ\nh3MJwUa2KFAR+YIjGwZ09oa8/rvL+ercxTycjahvazD5C0B6BB8baqM13vIx5HNQiplcnEYUx0yd\nZpnuYOXToxz+tQCkxMYOKQUP3r2E9q7llMtj/KZ/iBvmWXRjjCdLMRUTUaOBHMwwJzuTqBwRyQpl\nB9lmDieG2dRXxocRnUVFpr2E14rjZnTTGgjuvL8PuwvKRwS8UoXPD5Q48uXZHH/CXOJYcsfGtbw1\n3MfSE2az7H7Fw5sjylYhgvtYdnrAB+Zkbm3ew/j7Z3HDLYYLr7kHNeN+fvo/C3T3/QVHesU9w1ux\npsjU9tmojguIRu9HESLIsbMBD2yrUIsdPu0QViNIJgja5pBeIlyDlAerIDASaSxGsHf07MBaUj55\n7QDnQCiAEEODyMHgnjnMOfEVHls5k2xQRJk0AWlsPqKiK7w4VOL5iiU2GgiomzF8FH/kePyzKB/+\nMO4wDUc9jnEWpPdY9o7hvAIJUiiaQZBkTZ/YcCclRoEwlBQmPEaQH2PWgoXMn9vL9Dzc95OFtIeS\n7bfN4nP/GrLsqL9g/mGfRrs8K5/uovnG7Vx8/C/4x1UtTG+LuODK0ylVqlQaw2AitFcIJRABPHTL\nKEcdspaHLyuRDzV9myOiKKCzRTE0UCOjUxQ17HSen1xT5Pk3qsxvi3jmvb/jqKsivn/nck6bfQLV\nkqO+q0RtZz9ow8PzF9Oe70YEsF8Ksg1FyipabUhW5ZjsCmibQwOGCq1dAQ9vHqEyOkpGaV66eglR\nBYZK4GVCAMt3FamxlXyX57W4xG/KdTqDLLU4woxU2VGq8trwCJWGxUYeE8XEUaLsY12MapHMmJfh\n1+/ew9LFPdSpUvdVRNqiYkttW4lXRofZ836DahQhA01dQlwqoW3Azd/o5lsnhMzp6mF6Tw+deUFT\nKbLtRd68YRF3fq2LF8/t4b0Hz2L3M2+w0yoKsxdSE4L3qx4RdlA8ohcVONCGKFaITIHaQIV7r61S\nj0NGBkfYuV6z8uJFnD6tg539I4TCM39eB5/vynFwTtGZH2PD0BqeXPsMKIcZDilvr3D1mmc4fIai\n/TftHN8TI3SDzx3Wz+mzLuQbNz3HxKmreGTb51l902nUVj9Gac3LLF3wDNN7Qrb2Valt28VQeRui\nrYjO5ciQo1kV3NNfYedoA2EsGaHI6HTSF0gLtLSIwCG0R2qfJAHlEBkQAci0oClB6Qwolczg9y7j\nE92GhAKlKFX7Wf3W5zno4H6EOgIRFHEtih1Uea40yNZfWoQJCFRAez7L5DBPa/d/M5zC+IceY30y\nO48MXnqEsEmK9BaHAOmxpoHAg0iBB+kTKGhhWorfBQVSdnfiTVBbzxVr3ubeW06nevt3KeQt5Xs9\nSxeWWLpkMad+axqXnTMXZ6/g6mM+w9W3/BO1Gy/jNavIxhrr6zStxVkLSvLcMzWOO3qA3kciKlXB\n1ruq1HtDkIqf9Xbx7LGDCOXZNCj4XAwfNGBbydAaxIyuGWWoV3Fgbph/+tRavnXhIE891cGbLw9w\nzKtZ7lrYy3emL2ND+T608IReoL1CotDekQok2vdSsxE+HuWwGSGnzSlQc3WuuWEmr397ATsbI9y5\nchkHHDmTW+/biPB1CDJktEK4mCse38yTZyzkrWW9HHzyaoTVmIEIrxP48nuNmKxqwziLVQGtaH76\n2PXstziPHC2h2zRhLo+J64yMGX5vPMYCoo60hoyNiLRDj1mu/WbISQdMQ91xBPZD2P7AKPVoMTI2\nbHg0x2tqDdlckfaiJrZQqRgqJcv0Ls/Sni5iqTl6+jTm5zSbSv28b0rsHygK7UX0jIg595c5oMvz\n7kCVcF2FF6b1UDENtCtQKIaQt3hTQzlBYBsEKF57/n6iw0+n0JFj1uGLSPWupWkHmNuzkCdem8dF\n57zBD9c0qFT7+O5zy3nj+jdZtiiCNR9w6btHc8Zl73DR+iJXvLyaDVHEFm04fGwrtbEFtIcz2Wk8\nT5Qq1KpV4nQLXjXISAPEpKTCoRAqmZTREhJjcFgIFCqd+Jwa60lpsI2EzSiUwpk/giZLgXTgLXgV\n4vCM7nMib/3zStr2zCYvB8m6Aiq9lcykKqZeRak8WqXJTvLU/39IvE8YHx//Pxnf/6l1kFLjT39m\nX7SAyFrwEV4IMBKURQjAC6TzCBzG1wGBIUFCn3OJorjP5ZzTk+O0mV1MbZ/Phme2MjJaJUQx/5Qu\nlp71KrtH1jAlNIQ985g+7zW++eV92HUAVFPbCHwLeI1pRIReINDohuHUM/s59eRnGBm2XHpDG1ed\nWyRsaRANK844eTP3DEasX2d5b7jCaScoHt1o+MWKIv963DbCsIWto5Z9Xl/EIaetQWeLzPpCSOWN\nQf7+V72c2JXDVBS5fa5n5/AAI2YdAktACyiNDmFKsRsZdLGhdDeZhiVIFxjY535OnDeP8N0fsunp\ntWzHcu2iXkbKISs3D3D2xvspKI1PB5g4olJqsHPYcNW8dlKuzp7IEVUkQ2OKKIpxvsGOakTsYoz3\n/OT1H3Jwp0VqBbk0woAgRVMo3qvWMDvH2BpXMGVPPGbYXYoREWSDve5czqKkxlKlXlU0MNTzDmMl\nSsYM7JIEaOIoIlssMKutg/MXLOSgdkN95GkuWziNlT9aw50bbyebz3Pl4iWc1nUSV6xaTwi8Vh5g\nP5XhjetX8OM37+Hjf3Um19+9CoVFKEOQE7SqHOmsIqsVGVJ4m2hoNJynUEy0HB6rbMa2LeO2e98i\nKB7E3Q/OpX3fIS44/TiWnT3ID/7+hwwMv0qoNCoNNWdoDTJk24q053MUuvJkJ7Xx7MujWOvBRjRl\nCi18AnHWEp1WaCswTuL9Xgv72FJrxDRjiOqe2liVKPKYaIx4zFKLPbZpicYMTZP8/72F5BlusfgE\n3OQ8Kd8g39XBX318O9//RpV451oeXbWRYmEahQMzlH7ZZHupSjanWPa1yT8bHx+f+h/F45/FToEJ\nExApqBuLl0nBJYRACoidQDY8QgLO4fBACk8T4cFbi2144n+xVEYtT4z1E95yHbe+PUICHleE2VlM\nLt7D1s0VRvpf48crFqA7HuYz1/+GzY8fikvnQQnMWAlpTcL/FyFbtkY8+u56RioxP//neWx4SnFj\nSfCJbAcnfmM2UTiHb15b5uTuLDNOmMbDawYxseUrt4xCd8CZ501nSj7H8fM0c7/2LA/fNJOfbyyw\n/N5uZkUjtHd7HjrtYcLCbG7YNIB1AXPmBlgfg9DYGB77QZ3J0wdJ5UBJQUZb9MfXcnxPJ/u1eW78\nxEbYcjk+Unid5s2hQZ5tBCgJqqGIGUWlJEoLnh0uMSdQRDaN0zBRjCKIycg03V0dGGByscDBU7to\nWkOQkxgsoROQzhADylsibwhlIqO/2zYS8o8QGNJ414A4xgJ144niGkPhGLZi8FaRciC9QncUOHZm\nL90dXaRyvbw2upFbnrdc05mj2ZbjzdI2nrcKXw0JXZGtowJrDamWRLsTq8hoRTbMMv7BSewpj5LN\nKpS0xKOSTDFCGY2RBkdMebhELh8xsWiRhHQeUeGF6yQjNualu/q468y7OOTTf8GXb36IqNqge+bV\nHPXiJXwYzaHQ4TE0aFUJLdkLz2RvsCZGuWmJuri3pNAoqRHC4pTfK+QDzcCjrAT2StwhCIREe4ia\nnonpABE0qFUCrIpRaYNpglYCZQSR9MnO2NmksYlAKYl3hkJPL/tM3IfX3tnO1259lRcWPsgra25n\n6/oBKm/XsWYMCAjC/EcOxz+LpPDhhz4Z76mkanJSI71HkMyqhVcYbxHOI/dmewBpLUIKyv2w539U\nmJ+vUbNZalGO0KYRWHQ6pNS3lSuCi3jr85uYs2QqTfM3fPavvsX1j7yMcgbQRNUaOnbUCMhiqagq\nb5YqXLK0RMsL3XTtJ6lXqrSG7cyfvYhCz31QWE1qYheFjgxP3VqkRxfY8eYI//BkBtEiKc1T3Bcq\nNqx7le5l/eCXIXKO3iNL3LxmhInhGm5ds5mlCx3WOsoNjUChnKaORWqIbJowNgiVwQWJSO3N3xac\nvHgJJx56JId/p53OD7oYGq7w/MgoU+IyodAIaxItSx+h8DhGKVfSGNIIG2O8olkFIQKU8ijhsB6+\nOrOIxIDSOOeSpqfyeGUJnOezwvGLiQH13VXyUlJLV5B1Qax8ovDjZYJKjcfAGwwG5xXZdIDPSSaL\nAudfcAk3Lt5NesdKVqyoYEYbdOuQlCoxJwioxgNsqNbYLWB+sYuhXIZn+/qwIzHlpsBXGshsER0E\n6LRiV82SQSGMJAakidkZW3YTEwZVJuYkYd4g0o6mreLkMFN6B3lnx2wuWio48DqLfv0dTuw6gIwu\ncN7Si1i+737M/+L/5Je7BEKlyIgMKkiT1cn9KAYCFAgpSAuPFIqMDkj2sAkpT2jwaUXooBEqpHek\nxkCEColJkLlWYxoxggapTGIipL3FATXvMYlTASmSUsKIhB9hadJ+YJY/7Pc79rx7He3FLZQrfUyc\nGfC1FRezu/8J9gxuZevGdeTbQqz2fyLy/vT6s0gK+0zYJ8EheEnsQAqD8x6BRoiEn55M6AQNPE08\nzkMgHQbBloEG+/71CA93FVGiTDZII0xMrVEhLTVb1o1glUK5CvMrEdcv/ZCvXfQxrnmoH+MtGAMW\njLF4pSk7j4wMrdMFG/bkULFlt7WY2JGdP5vWLslVy1eTCUJWrIo587QVPLd+hMnDMXpRkdMO+g0b\n3gzp/v6n+PLta1m2XqNPHeDwUzLc8O15hKl+zmAmv91+Ce834Mknbue4QwY4Z2WRe8PZmMpo4oLl\nPCcun0G5q0az2oJsVGmKgOfuXsjzA30cdPKnuPn0JzlWOHw64KG+jdTi7+PTDmkdkTU0rcE0Ldp4\n3qvC1tDhrUOYKl54dJDFyhy7q5bYW/KtikhatE6ovClnsYEikE2wDYJJARNrMTudJVKGgIT9KaVD\n68S+T9mAICgjyLMlirnolB6+c/FcpltJM5dlxyW9HOsEkYkxtsptd68h83KJB1/oonO25pt3vwyr\nfkRn2MsNF5wL2vPUmipZExC0Gz7uAjqLIeKtq9k5/Cr/+LtjyQwockFICoNMtZEJLPWowlC/4q1+\nxTFfG8TXq8RbPSMFz+5excowj+8qse9vlnPewZKHL+vjp61LqJjtNOOInzQG+emL27hC3Y/PCrIB\n5JQglRaolEw8W12MlpDSCbqmScLYzSoFMhEVFmnQgPUOGyikFURaETqBspY4bXE0qQdAJU0KQCTQ\nZeGSZCAFWOfx3kM6BQ3LZcsvof3Qe8i3L2Dk6et4x1QQYymCtGKiluxXqPD+whDjVYIU/ojrzyIp\nTBD7kJHJGEYC1iVbNIPFO4cTDqzES5+4LCPAeWIDWsBOY7jvxRJqay+BjLFegdZ4BKXRGpUxUESk\npGHlVcdTtjU2VavsMRYTR6RsQDOdULdpeDAWaywRjt80HFEcg5TceccNZJxkcriOv3/KIwLNGcvW\nszJu48rTDSdXu2k/ootP330s8/7qaHqXLEKrjZx84WY2DVo6Pv97RFeBiWm48qI1fGfWdLIyS7k/\nx733pnnz9Gc5dkEX1uapV/qoSUjtCgiQlJtVxKSZnHzCxTRHJS8NXEd4c4YXLj+Xo49ey9l3zOPQ\n4RFU4DA0iYnxNo1rNIhiyR40Z58ym03r1tJ0jYR0IwOsTWOjMW5d1ceP7z2L156NUT6NVhCSGKAK\nkvssAoVoAAKkUmR8DW0tsfAIIbFVifcJnVvqkBMPDFm5cjHn7HsWT/VY6oNVaspiSiOM/NLzHoIm\niqmzQ6Z0dWAfTLGzP2K7bXIDmiMP7EYGsPT2zVxxYZkTZ6bZ31qmTNNk0prXNq/lsM+u45SNfZjY\nE7RBJgyp1S2uKSgWZmCqJeJ+T31slEZsqUUQ6tnY0V7q2nLng0Uw8yjO28JVD13AHF6gM7+QZpfH\nmjwL/+58lPg1E1VImFZklCDIpNGqQdZpsGlSiVsgwgky0iOUTnRSpUKLBOeCFDgSqTojbaJNYT02\nUGgbETmDRpAR4HTiUyK9J6mBPdbuHU/LhBjXBKZ0KH61+XaG1gxijGIoBiMdIiXZ2ZJHToSUsryv\nJEor+P1Hi8f/rBnMk8DkvW/5BPD78fHxzr1S8L8Edu4999b4+PjZ/+E1xsEIh3KelBCk0DStxexl\nf0krMdKSsgqI8GjwliZppE107884ezOrlhoEaSIHOqqSFy1UXIyJY7yPEHE/Z9zUxzl3nEVZjGJi\nT2yT7a6OE2enlI2o+xhhLNJZjJfkVciz/YLWj13Kt66bzQtveU67YQU6WMmLubP4MBfw/DOaSxd7\nsrqXty8+mksfu5NLNy3lyvmCU/dZwV0/H+DQZ+Dmi2dQa7Sw8qazMMSE3UWirtnY9QPM6ViCMgrd\nEhDXQ0IXEShPhEKQ56SZs5HAs6OjXFps54BCN6fNnMnky89ix/r7Kce34FSKrJdEDUe9EePrkhsX\nL+fW5b3M/v3VPEuITudJvIgFkbRsHa1hREDYO41CbAhznpQKUMqREuLffT6bDqy3ZALIZNLEu1OJ\nZ4dMZMsj5fCRo4mgO5envv0CfvHgTLa/vI0NI0PUlYZdjlIJnto+wlduG2BcjXFN3zQO//Zi6s0K\nUzs9c9q7OeNHw/iG59TrnubZjSWObAs5pkvRmWsj0FCNLA+ufoOomSZvNdujJvs7ixGeWYd30Tkt\nZLLqQIxO4x1RZeIVAwgr2b3NIWyeQJY5fJrgFwcu4MIVW+CKLOXhp6lVf0FhUhZ0gBOKY36zk2zu\nC7Tm6iilE/TnXuyASitiBF6mEcoR+ERM1ydMaFJaEhBipEG4ZASphExASQqkbhJ6gbeawBRpBgbR\nEmMrMSKoo+LEU7RmHUommpfOJ66oxsMLm/t54h/mogYjyuVharSgGaGQF4R5hQ1ifqsUhgafCQJ4\n76MlhY+CU3gY+NIfHxgfHz9xfHy8c3x8vBN4Fnjuj06X/+3cR0kIABP2mUCgFFpqFJKECuITMQ+l\ncCphQDZdAmTCOpyzCGuJvMP6gMpwkbiax8YaWW8gJivVRgAAIABJREFURZmKrSKbFiJHPPoGG/rX\nQmaY45cViaMKzit8U6ObDXzdEEURNZ+o39ZJBF6m5Lt5dTDLDasGmXpCEVRE9xEX0Jo3fOovBUcd\ns5xNbxSZ0R0y9GrMrz7+EF+4aR+s7Waq3Mq3zmqnvH4NX25XHNSVp12VmH+goz0Xc/i0NBmZZnJm\nNp3hDDat7kcFMShFWgQYK1DpDD7WFAs97F/owjQU20c1P9oc81oEVudJSYORFQISCXvfqECk6cz1\n0Ny6hfyvr+e7sxVnXHstud4CnR1FbEcR0ZamFluiiuC2b84mGyQiq2FkAJtIpluPkAIvBNpYQicJ\nVfKk00qRCRJEqbPJdjoVtJAN2xBYdo8O8uULH2Fk/o8YOvo+qkd/n8r8u7Hn3M7B922kpqo8NHse\nQ3OLhFYxdX4XGRMxJVdg7vwutpaGWfHQANlQ89WugPa2DNl8SFR31MbKDA56rFNMzAa0FlNEeOK6\nwMmAyXnN1KKg2KvYUjP8+L6IqFTg+C+l2eOHiKMSQ5sF15+3iu8cM4tnb7uHWlrig5goLhFFEd5E\n3HbrBXx9SRdBoMkHijBQZKUikw4QKkBYSYgipAWhFTIdgE6jVXrvbkyiUwqhkvsk0oKUkqTSEqFS\nKC3JBIqMDskEgiBQ5DJpcjpDKpAYbRM2qkt6NgBOSJTSPLmmj8ceD6iZbuooGKsSVS3ZXBqjBUop\nUj7AedBx5aOEIvARksL4+Hgf8CfhUBMmTJgAfBVY85Gv+KevgfCeSIEVYITHiUSCzOydNwgVoCWo\nZCiJ9x4tFCFpYtfASk/5X1exIw5AK+K6I92MEgESWWIoLvFsLUYHHUwpgMKjRSIYgvAoZckoCKwl\nVQE9DMd0nsCszGweXNVHebDMkV1daK+Iq21E8oeIxmzOmNRHra+KdTWGXJUvTHiKTNsgn7v4PMJg\nFlO6OlA6x4+vXcP0azey7BlJlLK0BmVaqVBwMZ0HKuZfvAgbasrDyZNgf1XAeIsTithGZHIBu2sV\nKrFg68gIqDau7J3J1tGYl1atwo6C9RmaLoeWIQ/ecQnFf3mTG8/oYMfgWj4961JKh67GNRW7cUgE\n3jrK2/oJhOXk2QE1V8UaQ4xAuQYaT0ZqAgcTrScjAqQC4zxCWpSF0HpCp6g5gbaSpnIIG7EjruIO\n7aNw7DNc/0Y/FWOpxVAai1EEnNg9m0eWLefOC+Yxq6cbdWCe2OTY7TRmsMJRQciZi3LMnenopIbS\nMagmzXIMsSWfz5Ob1kamTbDHW8JQ8UGseGfY8PCap/nOVVs55pyNZE5aywNrq0yZqTGRp31iFyfO\nnY0Pu5mzos4L6R+w69RPsHnfn3PQF+fiRg3R2Fpsow8zNkgh0Fx2wSKwARmVJD4dKEIUGo3xAiE0\nIp1CS0lGaUKl0UKB0iCS8WygNCoFSoBPJwGrW9KIMCFXBS2SIFCEgSPTkkIHiiDUBOk01jYxdq8+\nIdA0SX9gYNswJ5x8HRv62xCZHEJZvAtpzxRpzU9j6bkvcPMVGzh67jLe46OzJP+rPYXDgbHx8fFf\n/dGxz06YMGEQaACXj4+Pb/lTH/xjM5hW9ZdooajjsCoxulAiBVJgGh4voe5jNOCwSAFSJTgFmg1C\n5ZFWcMqNZeYccRkz+i7hlkVtCFsCJdANRW3I8/6I4OOhoPZexPR8gaPzI+zOgwk0ttWC9Sgp0FvT\n7Ew3mGwzPLapj4knz2FiIUtbociOi/q4583LMRT5l84WltxyCp96rZWVN3+X0y6+mmZzNpsOXc1J\nXe/x9bMuYPmlDabMXcytI8u5c2ARcwYds+6QfGdWgPJj7IgGObI9S2t7ju6FvYhcwmZT+Tyh7cBb\nSbatSJ0Mz/bXsXEKUNz7vYUUJmluOef7PDsySFHnEKHDRoYH71jC48sXUh7exorBNWR+8CrH3riO\neqDIj0bESqHzmrgBtubpLqgENFX3yCANArSRaBVDWuG8w4tEP2GiUWR9kLgUOQ8KqsYm01/2svy8\nAVqIoyoCzYygm9O+1EYQagYrlptPn82Ed1rY/tg8XrXDVKqSG+9ey5RCkdtmKF789kbA8xlTpB5Y\ndtxfQcwsoKIMQnhau3uRmTzGCyJbQ1TyqMDwmRbL6yMlnts8gso9QiHMUbGDPBUrbl3RxayudtqP\nCBGBYuu6Mhuu3UXnki7eH7+LI79U4odPL+d753zIbmPIxFVkM0SYmGmHLuVQYqQICV0S2EIJPBrj\nEiR8RoCSKbSQeCkT5yYp0ULRlC4BU6kMOE/WeqyQCOcIpSZWITIwWCfQNqSqIZtrYisGZyxxi6bZ\nAGdN8oAUAu88OMnDq9Zy2rkXsureXiZ3Vcm3NTl4xgIuXXYB+88+iaZJMStfY86ke+Dbx32koP6v\nJoVF/N93Cb8FWsfHx/9pwoQJBwNrJ0yYMGV8fLzxv3/wj81gpn7iY+M7AL+XQz7RaozzeBcjEei9\nNFLnwCpDyiqaLsY6kKk2aEYgxwiDDK+9UeNqeRHi54uwzy8kFIZGGFIuBdSq/Vyz+RlGcsdw6pmX\n8q0zLft1d7FHhNQqNYyp0l7QfHV6gfSIpLYn4pqT12Ebg1y4oI30hzm0UhTzlld3X4FXV3D8DQN8\neugRDjKKi+4t8ZvWiZx7+Cn8ZfFKbvl5kUOWHcx3Dt6PzsIF3Hh6yP5K8ZZtoJWn5rNkdBs7zCiT\nTYnQB1gpsQ2AOq35ArG1pJXAxZZICUrDrzKjWERMynHh92/n6qvup1i0iNAydVI7rQu7uHBmD7fe\nvpadtsTK76/hiUcMXg4yNV2kNhpRMoLufBfpqIIONa0daeyYJwoVobVoElfvvVofpEmDdzSsTfj9\nOJRNXKp81dGMBRkV01QKYROqb8YLrEgzqyPPDdd3U3tnMTvx7LFwWUcDM1ZiuxnjyttL7LZFps44\niyt7DPHA/TRmWTJtRSYHmqfWGD7/lZjm/5XnojMXYIwhrrawO9DoeBRXV5iGIqMULm8oFEOqjYDd\npVHOOGYtF16ygNOuUFzbNsox0wY5bW6eqT05sjMFmwbrtDdP56oLprFl8x28uPoO7rj7Q3auW49T\nAhn1E7u12FoVpoDAkvEBKIVEEDsFMikfkA6PxCqBBKRMwEveQ0aIRG7NgsZiZAbwCSZHOHw6TdM2\nEUqRUh6pKmilCTM5bN4QlASR8zgJNFJYfDJmlo60yvLw7a9ywpLvs2rlw1y1IYe9dzZaZ9hRG8E4\nQyVukApaPnJQ/6eTwoQJEwRwHHDwHwX6B8AHe1//bMKECWXgABJruf/39eE4Eou0AqdA+qSHUFMh\nEzE0vcFZhVCWukmsshwBMm1wNkI2Hdb5BN4rHdYGmPTTPPLrHNN/ey6nzVQcf9J0CjvzmNtLDO3y\nRI0GtUqFX5RLMLmXpnVoESXocmuIB2P8bM+R7Xlah8tcGgr2MMjBMzW/X/o8z1y8nhSb8VzN3z0U\n88uvn4WNn2dgeIC/eOsGPti/na8vOYFv7luHe0a5bfUMTl3YA7mQ51aXcKNr2f8LeTqn94DoIO4H\nYXdhSgadl3idQqDYE0WoXAFLhBmNaZqY1p4sWwZKbNk4yKxeRbYtTWduOnPmL2DpKW00TcwLoxHx\nTosfyBEEMZg8xUDR2VMkJSGu1DCxQxFirQXp+L1pcJBJI60g5Q1YTbqhcQ689DSNpWkbKOtx73v8\ncMTQSJBsbZGk7F7RUaWxFuYeWOTWy9s5LR+xJ4Ln+ypsLVWQNubK/ohMGo6/4FzOnraMg8Jh5DOb\nid8ocfPabRQKdeYUipy3pp1b7uph+2+XU2tpQ9tB9tgEzxp7idFpUs5Sa0CYVXyiz/BWi2DniKe1\nkOa10QH0G1105eC1vzdMvX2E90PFSFwho8ZYdsFG3vv5z7ho8w+49pImrxbvwFQGMWYQMxZTWbeW\nl9aO8vAqhfcSLy1eCGQjh8l1Iq0HqROtbaHICIVsSnAgvAAhE96KcP9ulmutAGEQPknAddsAlSKd\nhqapImRMRiR2cFmtUXlQcRWqAquaBGimdvWys7ye8pghEIKhzWWm9ESsOPZctG5Q8nWIE7drUzHU\nx8ofObb/KzuFo4DS+Pj4P/7bgQkTJkwE4vHx8T9MmDBhfxIzmP+w5/mHffZ+FeETXTURIKVFy4im\nMGiTIlZNsJqMaqEux5ANgSQkUhGPXNHLa2+t4LZ7P81Pnn6RF19eyxMP/IjJJ5zL6HCGg747zPbL\n53FQto1bN22jXo2YHLZRCeEAchRyXTxXM2x6ZBQzUuP50HHZgOebazazQ0XM7cniY8OTq99AmJj7\nHv8dgr/BMxNBivOv6OJM+xI/vOs6up+Yy9/8yw4ePlvx7cxutrywP79f5Rio9HPGtyUmzLPfjMWE\nchGTI0O2ANN7Mgz1jVB3nkhYgjEPYw1SKkBgqccGJyymHtHZO5OtAyNcePIqvvi3B1OYOciPb15C\nrm0Sk0PDmR0x2VBy45KF8EpMpM5Cf/gk7R9YHtv0IMd8vY3ytm00B0ZAKGpK8kqpwemzmxwC6Bji\npiIVOpTwWJOg6HAWC/+O5zAN2BJZKgR4N4qSSXEnpEFYDUpxTLfiUm25ZnXElYN9xJcNciT9HD6p\ngwGZ4/GbvoevlDj6B3dx2MlpXs8oqkOKPSZNbURQd3WWfmMactJJKGXY+rZhVk7QKjW7MXg0IpUm\nlA3KZXDC8N5gia2Dht0OOueFqCMUb43E1MuCGxb0cNu3Czza0saW0hg7ak1OW3IHqbsC1vXXmNrV\nyY6qJrJp4riBGa2xcvX3eP7ZgxPToYbAC4P1kFV5IitBBjjhSREQQEKhlomesLaAdtSVJIVFkkaI\nxBfVI1EquZ+1dAsp38DaOiml0BmbTNBaGvhqwOQgBqFoGIHF0xq088jh7/FS43kWD87HZws8/vr3\nmHvwStRfN9hTq+MwyJSlGQmMjWnwf9Ah6k+ZwYyPjz9EYjn/vzcYe4FrJkyY4IAPgbPHx8f/Y87m\nHz7c+2UUeAci6cArFyBtAlX1BlAmcUcSEqMcOp2ms1FlwZIcm+0izj9oHmJOB5/0d7Kdp/jp04t4\nY90AF171JOcsaKdz2gaWH38PZ990Ghv6+8FIdh9Y4sJgMSfPzfLKM93ovOedchUCKI4pVFGQUpqc\nVzy3rsZjz57FLX95P5lgGzuHDyfMLSSTf5D7/lkx65fTmDptLk2l+NvVv+Rff/sV1j/5A7581seo\nfjDArJsXMWPmTFrDFoqTOrC9AhnEDFUM9YbFKkE2XYRqGY+CdIhyATZuYJuGzjCNlmPc8Ew/B39h\ngAO+eBef+ux6jjuvj+KHF3Pl4pjolyWefbyfKbkShGlMOiYQObQWzJkXYH7XywsvLGDOlB/x6MuD\nBKpKTRmWb4Z3v1IkH8R8xgaoKIMJ9zZkdQIeU01LuWGIIsvvSg2WDQ6SUcMQS6xsJI0wozAYQtLc\nvHEb4syAs1dFbLIxc3rn8sLKcwk+2EFLVKY+8ipXrO3jxtM7OLStncnCMn9OF1cMlTjSW6K/EzDv\nLFJKU+4fZMakAKUFJhtBXaOVJxJAOkBnY9KigAjyeGs4cprgnqchiB1CaTbFlisHR1m5NebxFa9w\n+wt3cO6sWcy58wGOv6Sbe6/K8+JjhiiKGSjF7Nj2Hq8ObuaoY67i2KPvR+qk8Y2X4DRlZakDGScB\nA4SJlqgBJxwpkUY4aJLCYRL1a0XSg5CgMECAkQ200jRFA1B41cAoidcRnoBUIBGDVVqdpZYV5ITF\n+DrfP/fT3PLkz/iH4F2eGGzhN5e9S6E7QtVjIlvHk6UZV3ENgzAN6v+PAv7/I+b/HAhRXX+txrd0\nfQ4pJQ6BcioZd3hBs7FXd8nHWGOQzhIbS6At0z9cwfTiEqYWHJeeF3LZ7FSCOVAt7KjA+sGY7aO7\n6cx3gqmwu/oq0WCJ5bdfyktr17J7tEpGSVY+ejm7ZZqaqzJUitlTiyhXqlgrCX2WaCwx+TBRldmH\nvcKBFx9GvO4w3r7jMK59p4OXf/opIv0APu5g0+Z1nHPDPZQGXkVrxcMXnsyXj32bU4+aQiqXphk1\nyAhJXbmERCMbqF118DETi93URwaIGhUK+Sx1Wqhs76cwEUgr5vQERK7BK4MB65+JcfvejtSS1380\nxPRshdc2rueJNwbxxqLyBpeGHIJUoABPtr2dlX13Mb2ni0D1sv8BG/nh+WvY2ahTtqNcekKRpTM7\nOLqjSHs+jwg1KgwJLcQYvHVEpk65Ai+WxniiNIIUkIo0YEkLhTMeAkUoBF4m3pKeBpd98wJeefJc\nDs8rbFzikPNvoTHnBwRDD/D481upRBkeXPwlZul+jBvmyucjDupexFUXLGZnpUwmiPENQzxYIZRQ\nthmsteyxTRoqha8mpd9zr/ZT7qtx6gLFkedvZqfzZFqyVEJDzmmOPSJgxsFf4dOzHuGACb/gkMPm\ns+gbhl++mufnTylGSjA0XOLJtZt54PFbOPb0u9nQPwqNHDadgOLwbciuuTiRI+/BKEFmr0+JkIq0\ncKSExEuB0Mlvl00Y/yQGUB5jHDQ81sfUbZJo47hCPbJEsSWqxtSNxzQszrTz7MvraapdiCiiNur5\n2MZDmP2Jb3PTb7/OYaevJv3JBdQjSRQnWSdqgI3BeMtQNIodk7i3HvrvQ4iaMGEclMV5i6aFZlqQ\ndgpnJF4FpLyniQIVAwatFKgx8vv0M+kX3+PXvy5yzuPdjAwOUn57KyvXrGX/4yQ7FnSxs1RipPIj\nAqXY+fYa+vqfBl/m6F6LySm274oY2tTH9IWLEEGI8yGfUW28Xt1MoajYX3p8UKAeW1KTLKtPqXDi\nihZ2nPo5ug+JOOGtKmvnZLFtryN8htrVL/CPK95j45oXEI3JrFi8hB+oOURqgMcfWMvkoufsqQWq\nDFAnT97mqdRHyP4v6t4+Oqry3vv+RK/dznWOe46zW3fqTGXQmZppSY7JkVDJKSChEgXEWMB3FK0F\n3/AV8V1U1FrEV6T4QhFRDqJgUQMKNljAk1CCTWxCndiMddLO2Gy897Rz2XON7kvz/LHTs/qsdd/3\n8V7rPPfyuf7K2mvN7LX2yvWb/bt+3+/n29CMURpTzqIGO3DnPsjqJTupR5PLFWieO4m4G0F5AltK\n5l2W5KBaT0QIDoxzyfUWOZBvIVnXRDzhsn9fO+5QlnnnX8SodBA+s4Lh8N5ubt/3CE4iwTlz0vzq\nHY9Pj34Cz3JY2+Yxq9DBNYl9pOzlnDenie9lGyHqogOLnF/h/W7N/rzmZS+LDGOAiThghqrDQzPb\nRwYSHUDSchFRm4vnT2faxjlMjuV57vXtzLrhRUYtPI76dAML751PMtnAG4/OZnqLT6RX89DzmmMK\nCZpnX8eOvu2AQ954qF37qMHBrUuitKanUCGejkFZUyhpPF3iWJnmrie3UxxwmTejlsvX9RGp1mSk\ni5t2cDMukhyOeIYX2m/luqvG0PluP4dVt5DtjtGTLbJty4sse2ID63dkueL2Lop+FH9IQSFPv8ww\nIZ3BoQHHaILAARFiBAFsK0BgYUw4SZOWHZ4pGJDCEFiRcExpNAqDKfvhlEJKkC4m4uPLAMu2sYMS\nY0QzR//rR7xc00H7hxk8UcJ3fJrmvcjgswGf/O5B9u4cy2kn+5T8MKWdQIIaQmuJqFiIAY+PA8VX\nv+B+/FIUhe7PD0GaKFrEqFgQBNEwI9IG21iYIMBSoWHKEzJM01FlnpVbWXPKVWxb1MTkulrsDwQy\n2sW2jj58bGZe1MrUOZPZ/e5+dLSPuO8yvjiO63eVGTtaE9ihVJT8bpzu7Uyb3kofErc+wfH7pyPL\nWeJRB2VrjkQybVqSPX/cR31HlB0rP+BieyLfz9exM5vFlJZgB2Vibh2PXjOBnL2BDyP/yrdmvYmy\nDWOSq2n7p+XMuKCJlupVNMs0nnYpfgCpeBM2FUp+Dl3sQuZypNzneezOFHvas4w6rpvceyeDSuIH\ngsJAGDefqq0mUi0oegp0QLHcjfDD7IeeHR24MkHuug52d+3mpiCPX/BYa73OKG3zTtbn5luu5ZI7\nHuS4x0/migvWEEsIag2MN0nOObeL256xeUxtZYwT5wg7zSdG0mXK5I3GwsaSIQWrIkEM+ZSVwAQg\no4T+FGlT70Z5e0Ur+YE2LthuOHbJSk68eSvz5szmwIMtrN/WSu24BuIljQi66c97XPxiD+NvjfNW\nk0ePUihy/PjuXWx+PcEgknqnROzdIXxjGJ/IoPwsFWUwSrNwei2Nl0lWt+epqbOgG0ppqJVOqCGI\nSnZ0PsnWlVFKubvJFzbx9tJl/PDcNgb9ifT1drCqfTeduT5efrKbgjFERBIjy/imnlGJ0TQmaxGB\nIBhxLwbGIiL+tpWsEe0CSB0gDGgnlDBaQoTUNm2hAyAwgIvRCoEJIV9Rh6gymKiDNAWOKGfpb3+V\nQ6pfYfTAqXgFyDjVHNWwgBVnfoP6RJmj0lfiaUNMKSISSibAFIrkspDI2Ehpg/ovw9//c30pikLV\noVXYto0xYSRcEEiI2qGsVhsCGSMqwwnEKAODboEjKhYH/SQXvfYiWz/dwIYdTby+aDzzJrXy2mst\n9JOnUyms8vNYTha0j5AKSwqsbJH773udKx5qxRFx/HiZU85czgnjnkRtm487OoGsCYh5k1DSZX+f\nxNDHfSfPxnb7UCrNTUvbmLwgQe4Zj/W7CmzevYmU28uP16/gzNYe/mn0Jzy6/lT43GfZ6X288EGW\nD19p5Herp3BBxiXvuhwcsIglwa6Gno41xO0Mxb4so8a30NXYjGV14h25jm98fydXnP06t1/eRKp+\nDom0S0walPLpy2m8Yh7KZWxyOFGFiAeMrbcQdp7n9FYUkTDUVRQgcEILslEUPMEldy/hBxNtVo/O\nML0WLpmUolEmWPF4mpopElxQ0YBflrM8l+3Go4SHjS0lCgvDEHHXcOlVCTb/tIDX60CgQ6+Ko8lr\nzTULu9nbkeXcMbA1K5nc1MDeO8Zx0uiA9QMGaSnMuz5bsx2c+exuOnOSa+oayPblqK0NmHnpOmps\ni0w6ScGDzR8IevIlMpk4IhIefNoujJIBg0Zz+09mc/wJm9j24j4OOjAmqEYKSSoZJW473PtkipUP\nXURQgMcXP8Jdt86lb3sd4pwkne0d7N01wGBWcsWv23i8rQ8KHqMaZjN+0hxS8QDhOFRCsT0IH1s7\nGFshpMRoiR8+NlRYAaCssbFQ0g4TuBEYKyDQPjr0VIJQRESYNm3bEQIzBFJyMFDU1F7A6qbHMFtO\n58ePfZNl6x5gRdsGvnn8V3j9JcXpD+eJWyEF2hIVZGBxhBSkHAtLq/BHtfzFcWxfiqIwXPW3MFkr\nzHmQFgGSiAXGkuhAEgjAJLDsAq5J4BuYNjFg2pSzWTLO4q69Ke7cLfkfW3Kce36WjXGFLX1UIIko\nD5kvYIZKlEWek86v5ZqVu9jblmVyawbbhIEytz67mOjwYtZvbMH2fBqlQ59QvN2dBeFz4hNbef2u\nVp5auY8FuZ/ysuzlg6pGDhkzmv/4y33883TBIe9sYul/7CWnVnDn939BvtTNgu8ezVcvu4q9XbV0\ndbVjdAMZB2qbMpSkR+AFxO04pf59UHCRqeuYd94DvPvZcn7fsYkd233+/KmLe6MiloxRVAXyAwOM\nbahjauNEah9tQiuNHxgqhTI6s51coQtTaicgjsFQ1t2UROgGraDp1zAofLwuxYFCF6Ywl+emF1jx\ncB8LFylUcgBtHLxuTaWgKFgOyUQjWroYFEYpYo5i+d1p1Kebebv4W6Z/ZTnb9XaKQyM8YgkVCx7b\nlKWTMtp1mJXMcPPZKeZtlrzRITnB9VEdPjnf58xHd3Pn7m6WtlzE4A6LnmLA+NmGH1nVePjYSUgV\nNIs3rSErHZ6ZP5/+UgGfPDoiqJcJ8trnR2eneXR1ioX3ebyphyhZBTzgiLKkpDQbt9rssRfzxodF\nXtm+m9ufVkw//qe46ire3J4lWyzSk9XYiSw1qRyeE8duSjMhrlGWTaAtXBmSljUGW5YxFqFcTwok\nUUrCIqYDwEILiZagjQIEthjBAGBjWxotJAKomPBANyKBShopDaVAUDP6ch7YOJ59m/IsuOJ6fldu\nAbWG+z6Zx8+3zGej0chyGWPJkVAkjW9bpJyAQSCiNIfq/3uKxv+WdfwwfChtlHBGECoxRBjHScyE\nyGtt0ijHwSo7uNWaP69tYmJzL7tOuYffyCxfeSNH7cUrSc5pJRHvoFDMg0ggrTDa2/Lz+NqAcYmM\n0sQubaI/KIBlsJDUNkraXg7Ysv9uvn/hTBYuuIF1Kx5gXmYN3qQ6RrkZBvvL3DZ9F689dCfHerVc\nvG4X9Y9WiK2z2aabGCvmctbiVv49kaGY/RZ/OuYpJs+pJd64il1//S2d/+DymbiTY2ob8AsF9nQt\nR9uahBO6FeOjmsBy6c9uwV6ynQe3reGgLYglovAnG18lsCdGQdtgJD3dvWQshx2btjK1KUUqkyQ+\nbhxjFyxCqcPZu28bnp/HTTgE/n5yupvNWzah+jcRG4oT83aidYn1z3awcaPNGz9McNbpHqva2uka\nAK9PY7RFjCRuJoFIuLh4KC2YeraNdchz/GrG97l0/kzETTeyvxumTzjACf94JKOqo1QC6z/JozaS\noq+4fkGK+yda3LW9xNS5LbB9K55Q3L5lH6c/u52EneCapkbikxzEkIffDV2XlrmrLcf6JxWPt2Y4\nOpVh8U876OvI4yR8vEgvlpUhb2yoBOxOKiamkox9YCexn5fRZQ/Pg1RUU0go3hceMb9Ej5XBrQt4\nZu1FLN8wSDF7Fu/aLm/nAzYWLJLOAGOr43gVi1QyjjIOtqWJosK/tYOwwCM0PLnGQhuDMGXCTF6L\ncIhohdoEY5AiNJZB2D0YrNAopRy0ENhWCaLgWBItAkQg+WPvq/zh03H0f/I9Up++xZrODbiph7lt\n5XYWLOxm3qQkIz5upJGAIG5gEEXMF1imQoztfG6IAAAgAElEQVT/hVfhf7K+FEXhneGqEYNoANhh\nUxoOwwAbhQMIhIKKEydHiVPqBfVSc9KaTRzwFcoU2OE0cKa9lXKxzEEEQgcoNYAq+DjaEAShnbhC\nmjMuz2NZaWK6TEQYTjw5waavO3jKoi/vs2RRK/MWJBDap6/7A45riYNWbH1Zc9aEaxnTMJGaeU/z\nckc3769K0Zi+ignpVUCepTcmaJgynlk/gtJQD3kTZ9aMr9JzzFOc13JR6HpzJBdfupire5qonS6p\ndzPE6xro/PVuvI7dnJgpcM4nNk+1F4mVDefNaeAKfT6UyyhfEc8kUUoRsTP8cusjBOVejqiG+qTE\nTR1OfVMziEeoybRwbjpFMplCyknYd93Gxy9v4Pk1bUy94VoO6AE8r0xPNsuRyWquebEDJRxqXINb\nAROBeNRBOALKPtq2qE8mWP2TFr426n5e+MUFOPEWMGnqaxN8bcxDpOzNlDxNDIHtK5RjRqZCgtfa\nDN/9KSxsbeZHmQqbHZ/bt2b5wc92Y7BJuUkmnO1yYIvhrT7N82du4op1O6lvqGdj9wB7yi3UJuuY\nIBNMe3o7nif5wWUKW1nsyGpc0uwfctj4VIFlbUniTkBFGoQEYQegbLRK4Mam89ZzDVx67yJWDO7i\nnxv+wvmnHMP9Lxp25hPYVgNYkkSqGieRQrgOXqCxpEs4ggwwRiOtMlCNbQRaCGQgQBokduhDjTAS\nZKSQBhiB2SBM6IoOwMIB4WPhYQmJRTUEZdBJbCk59pCfMzVZwLsqwV82HMVfD9vM7GsDLl8WFg87\nUDiWgw400jh4WoEuEwSErE8/j/p45H5fYH0pikIIWdGAQWBhG0FFREBINC7SkkQCB8vWBNLFLVSz\nox4mTxzHnkclS2+q5d8WnMWE2wUHClmwJFqE/Ebt+xijKSkLd0igdQGiYR9PoLFkFGUbxqYamLu/\niXNHDfDLtiz3rE1QIyXGqyM3sJ13cluJR2u54Lvt3HFfF3/8/TxWXF7P5s5aXkrkMGYxsYkWF09P\nsnFlM/GlbeTUBM5bdDXnzdjMoVXNaHs0nb0DJGUCU9jHJQsXM7UeYpkkZV+R3fAIe3Z3IbI+Udfj\nmdsaqLR5qKIilmngzIvG40xyiHgeB8uKzr4EUeNzwdy5vPxIGjsYQGkfky+QNz7YRQZ9l5MaHMq6\njAkMR9cdzlP2QrZ1n0R8aBN5I8j58OGQQte6HHogx3lzBKOStbiuBVKERjFbEotalGwH7Sc55phN\nvPeH+Zxz+lu0vZpEOhmMNLz+0icM17zNicccRQlFSUPMhIpH17LxNIwf38CF8yW54gcULt7OiY+1\nU1QVpLQx3Xn2PlvhpVUZsoe38Z1T7qbzogRLZziMdxzeyCfI9ficKtO81TbAOaf2QTCepGtjsjap\n8bPpyyguuVLw9kubOWrMTH68TqEcSTIziYXzn+fHt03ilvVv82+vfkTNn3dR2DGD2z+6mBPrFT2d\nM7h4TYT6VAafJPHqLD5ppEyhCwpXJrAT4PsDqDIYESVCQEVoAstCokKqtg7FVZoASygE+j8BzUYH\nYMsR1aNG4oUSZmODZWHjE3HqsKIpOge62PTJIyy//jo+F828L2/hhG/OYslRtUxuWsq6f9aoUhFV\nssi4MXxbIS2wtQwnHGUDQhJDM/QF9+OXoiiY4WEOC6qxpI0Qdmj6sCUlLRHSQiCpBBaeTIRo/EQC\nrSBfiHDdlP0smeKBP4p/vNklN+CDkbiuQYgIpaJClEMgfKACKFvoQGMiYT6ikDaCBJiAmVMdKsSw\nPnEZ/y8eXm8Bz+qjJi04Y6KD57n0LM9z21L43a+KLE88z8vBeB6/p5GYSVC7rYFLJtns6T4Z06U5\nadoGcht7+H5DCrfh31mx/0y+Un6d1RdKitJj7L84xLCJKou8p9nRNgC6Am4MmZjEklUBP43Pw47C\ngfxnlN7tpDHp4qY0T20ZQJFmZ3c7ixedzZjnMmx+tgnL342tFcIBQwwlC+zsG0Ch+Uxrdnht5APN\nBcogAkH/jasYc+USjh+d5LfFJOctbeecSyQLr1BMbsqQysSRQmJHQ8y+K+EIqXGcOC+s3c7jDx3G\nab//Ez+6ykVE88Rc+PPn3wZZhV1WFIFIGaLRMibmMMpxWNiq2dGR54S7d/PmiwWEMji4RISm3nGY\ntXIX/QfHcdhUj31lgd2m0b+Evi4f1x3H420D7NjtccLlLl6unfvPy/DSRof9Hb1Ed2zlUK2ZEE/g\nqu2cOuoYbsppUo2Ss56ZzbM7rmdC60FS6bNpnp/ANQHFo37MFfM7eezpdRTb97GDJLo0g4qcQUxG\n0TKG8g2hRVIwKgk18YvY37cTnwI2DqBBaMwI5UgAFhoCiyAoh7lnGgJLUAHsUvherAAhJEYHBPgI\nAZ6U3Hf2bC6d9zGXXvAxfcnnOP3mnZz2rXc4YdIMiu51pB7JsuiN9+nvSjBttObAu2WSbhxLgQkC\nJCY8t9AKaVl84dcEviRFgUM+H+m9BFJIAstGYRGJ2iBcjNFgJYkFh1ERLrbRVISkJw+OnEDH3oP8\nw2/XsrX3XP6g8hwpRvT8QQKpNASakqcoeB6u44aJU8ZHAAILadtY0sHGxXVtrpWaK2q24nk2Raua\nMRKaU0n6uuBnV6R58XHJzTNcCnMivJY1nLA/y3kTpqApo7TEtR3q5XzWPh2wZ+t2HMfh5M3HEkmU\n+NF3GkhKjfaTiJoCwoni+z79Xh+1ziTiNQ3kvABbtnJ/f4FLvAeJ141j1U8msn/Hdma29zJpSjX1\n96WwEgnuv+U+XnvufC6+/FZKrRfR1zGXUimLED5OrIhDFFtBSfkcKgrs1YbANzROrOfEzCRevHM+\nh81s4MAOxT2Lu3C/sgbPBKxYl2VmY5nGkxNMaJiIkB6uLGDrJG7UoxKVPLTkSXIJQcvYQ3jvd1/j\nTx8dyeTTDX3+hYx+8xucO20erpYUHajgICR4vuKMa7M802F4uT3ANhvQlqCM4ITqDNvubGF1u88B\nJ8+py9vp6XCoXwMHOvJcvTjLG6WAIx3Yc+JuLrk9zTEz+zh3iSSxWnNgueLGhT53NXjsOSRP10cS\n+9k8z2QFYwc1xaeW87UjDiWe+AZIg7ZshIH6SS1MOfc+jm9czbw1DcSTClNuJ0IrkMEIG13wqeBh\nA8aXxOs8TkrM5c3dT4IuhxYlo1EB4XiR0DYNJoyJtwiF2QGARluAlthCoIIAg4Yo4LvUJ+soDEgu\ndQ6w9IZtnLP0M/7YvZrTXu/ikuMv5pm1TWz7ytMU3y7yQsdF9L9+HxHHhM5JE7JGFH87ZTBEdBkt\n/n+GYxv+nBGUu01gGYwIY8+MdNBKIkUCweEoJLYtMSqKlgFWkEaZCGedPIod742npC3wLf4s4FAs\nbOOHMlil+ASfQwWo8t8qe4CNBFvjxJPURJtxaseRrAjiEU0qcRVd47NUjIetyghpURP3eamnCfOu\n5OLbyuDUEU3GqDgeZ6KBIrPOdvjFb5/na+e8wpVfuYPobI9rbrqKb181iyd+0UDzT6ajVTue6kEL\nGxN47O/sZfUaxQt7V3HEyWm8NVt49dkuDu/2ufemOqbOmo6FRouAl1/cycMDu7iiv4lZ503Bli4P\n3r2GE16+FTGQZ1rLHCpz59LTuwVf1zMqGcVEAbdAXOW5psFhcksLmQ//gT+suJOu9i388OaFvNeV\n569f76CzV5CMukxNpil6Pjvb+jhthsALRlOf9kk5Pl4QxThD2OkMo2SU93uyeB/t4xt2mguntnL8\nL3/Bypt/SvtGjz6iWMpQcS1sSxNzJK/1aTxLk0xbCL8NWxio+GQyDUTHpxnMDfHQAwWwLFKTohws\n+zy6uMCdK3NccmkO3epwySFPMjhxgAe3QMa2ePXFAqee7DJhzRauv7idU080vNkxg5pxNrkSrFeK\nk+bt5pebV3DKIW/x8IbzGTs7AOqwpGTJ6pcYfcYC7NkSWbZHQLQ5tN2IrQfICx+tCvR12QiTwHUF\nkyc2Qk8z2/ZtpaI1EUJCmA2IQEEQwVhh6xSgkDj4hJO2kOAm8I3GCiyEyQASVyY57einmNW0k0rW\n5ebLx3DNnDxuci8l2YAuHE5/Ozw8aRc/fLGVi1tns3rXGhrTYBsHAvCNFUonVQAmgtFllP7iW/1L\nURQOoQqhBVjhyamU1ogoxEbYAsyRhOiRBFLbSBEweZLN3p/AE+ek4OU8ttbYfsBhJCmVejmowbYN\nJQORsqasBR9+bIihiEVKjIq5yGiGWLKJmRe9jOtex5sdN4IFtXYeU51mQrCB9/MBng02ZZIZSWpW\nM6UKXK/hQD6H54W/DrFcFtHoUJ/1efm+iZz3m88oHH4y8+66hs37DJmjn2L9HR1k9G4aqz3WEuAN\n+uzvbueNh3dzwb23sfj9+ajyAAd2d+B153HGCVLJBkqAVh7PbNiKypTRqsQr7V28EN3MuVdehBzc\nyW3Th3BlFOU71GRa2L1d89qWLHc9mMGNVZMvpIG53NwylxoHOjc8S+7KJ9n8yXHUfH0Nf/q8RHGf\nJmkF1GQS1KcjKC3xglqEKZHNFzFNGYT0iCfKITfB8UJXpFvNmLikogrMm7WcyccHuEf+iPEna0pa\ngx+AlIxyojiug691iEFXmglRwZvl3WgRUJ9JUpJpfF9RftfDiACbCGWlWbuuj4O+4fv3bMdqb+YP\n2Ra+e26O8Y1wQkYTuStLMpUmZSte3pLjtJYmfvVrm9JQQMEyVHzF9QMSPWkxu/qO4tDcGewffwxj\nW/6MwCJix+j8y7e4uVSNbyWRQQtxWognM+S8Phx6UYUW+ulGR7qJxydCSVEbZNhrd5JXBQRlwBmB\noVgYKiNZkCYMcrE8rAAIQDGSHBsIxmfm8Ob6K3n4pdmMT5X4/l1n89EfBSaxiJr2LbzxxAY2L/0c\nxz0Kr6OVeDrB+BXv8JVMmmT6GDhjDHEBOvxiEDI879AmzLFUGvnF/VBfjqLA52F3ZQlFMEKdDd1k\nUDAS2w4RHjEjyAU2mYRL0+an+MZvjuDtdwdY0BbFKy1jfNNSvFKWmg/qUL6maA0Q0SEcxFCmEpQ4\nLJYk7iZ5ozOBnWrhpMQkNndJLj07zXNbdjLv/MvYnc+TmdiIJOCIaECkYKECgW98wIFIHlO2EbaD\n7i8w2NVFTz6Oua+De290uYs5fJpo5HvzH8NufImrf/wLll+7gdWXp8mWPSK+wst10dNW4JO84quT\n0lxbM8Qr7kpGWQmmNSb4oRGkSBBPpfAlvLa7iwcfyTNrSoZMKsnm9m4iAryix7zW2fTLNHkFg6KN\nkmrivLMbeKV8OaeNm0FfwcOxNZad5/RNW7nl1iae3+mjlrr05QroYhqXDixHEFg2yaQEG3w9RG0y\nTrw6Q85XZHsDRqUUeQuSUpJQGiyJ8EpUiiE2qyYpiTsxtHZwGwzoBpT2wqJv20ipMUpRyYfKy2Qy\nzdi8z5t+O6mowwEdJ1/xcK9QoUFOhrxDFQgWz8nQssDl/osMp46zmTVFkNoClmuz7AmPsW3dPNrW\nzr3XJugrhPj0+tFJ/IEs/WUbJTWlgRSm+h5OuPqb/CYxG8yKMHiIKPWtd/J5dhn1iSz1oxfxafI6\nRiWP5B/1V/l6+jj2tx9H8zceY+fbVzBYMRT9PK5MEpcxvKCACDRahnh2AhFmlSCooEPpcxAQYIWt\nRTlAWIZSWbN+dSuPPjLIPz7Zyy/9LMXcsyx7/QDOEwEULsWXoE0XxfwmTNdYYkES23SxM5sjns4g\n/ma20h6WGClKlkYHAVorCHnoX3g7fimKwvAhYDBYRmIJg40IzWgW2BZgBIIECgfHkmR1hWNvGM8J\n940jh8M57Rnqj7+bY4/LkdrxFue25DljzQBn1t6JVj6/zBpmJlO84ia45CdPsaItx13bp1OZcxGZ\neC129CwGe7O8PTiG6+fG6er9Ce7PFxKrjpKMK3SXpH6ii9GGIj5CK7QsYRtw4g6jRC1aJnnm5x0M\nZqewx2nhZb+Pj+bdS9drLZSd77DoqiS1ZUXc0by5uZ03nshxVCLg+NY0jU6czsEszqhqVMKir/QB\nRKpJRBP4uoiRDms3rGTh3AZmTppIMj6d+zc0suedZaSaGxlV7ZE6ey4PRnaSK3r08Sw1tsulLXNx\n3TqaU6FhafWzWWpTDeTdgPPOSqKcKVR9HHDwvS6WX9zOnet2U9I+o2RAQgcUtUWNLhFzotQ6LvmC\nz47uPD3dAfWZKFMn1TLW0dgYjEoiIhpkaAIaFfGpGAkxQj65sDElC0/nEQHEYi5+uUgFn9qEgxQB\ncddCeEUGswXcShdWpIzCYlrLRHS+m1cfbODN++PE8fnFrjzf3VXgnJ0FFi1VTDgZpOPSOKWO2oWG\nzt4877XlmTVR4joOXqGA0TA2OYmf/amVv3x2AjWZOSAaCWHAPjHH5uoVO5kwaSLNjbcSy6zHsuaD\nEATkqagS7/12CEcswfv3/4HOLKLo+GH0o+UDNlKr8BkIE8qdgxECuQUw4uEZ6fZFEKAV9HU8wnPX\nDrDx3/aR+yf46rdTHP3NmbgyRdKZToDAFg521CUq00SII4ckRjlkvTtY8MQSHDuB0uApjREagoBS\nBfwSFPOCkvq/w1P4b1vDw8Noq4wIyliRWBj8YpnQYhJEQ024JRGiDL6DZWJc8PPZBLqCIsYCY1DZ\nFgLTwm9uu5AYz/POxRN4JnMlXd4U8vlmHu+FQ0/byveud2H9FP6wrYkzW8Yy+bxH+dYlr/PR3tUs\nm9xO4YOPea/wCMd83Mp5sw2Foe0U8wJvKMAanSTyQQEjQtgGQZSIPYTXleDMiTbvX3YnF09sJp8v\n4FFg22KBp4/ku64kofP0lIo4kRh9H0T53kUXYnU8zRu7A46fUY0qfECyZFOOaBI1kxg1rpH+7u1I\nV/Lm1g2ctPJ7RKtdase2oOPVHFtXR7x9NwsXzGdw1wbGBob7Lmphpqwjn32d/L7dXHDfTpavsEiS\n4tLzM9Q+NY5V9m76vdcpbveJuRZusszNc11s5yJmH1Vg5g1dOHYebflYBYGqlNHlMjoKbkJitI3y\nNS+3F9i4KUvctRnblGb6lCyT0xmkUiFpW4KJuASW4QgkEMUizztA3NF4xmCKAu37JKXGTUguiNey\n49k+rnkxy55ImQBFyqnllqsTnHNilm+Oc+h3LVY/0cPpN/eydqdP54BGonHPqeWas6NMbc0w9Nku\ntnV8wMe9HmqFQywRdvBaulx941nct+xfuP3eIgRgyIPsRugCMpA8t+EtrH1X0jWllXpOQjpTsOWN\neGoDpcIG0Gu54+4sz76Qo5Ttxc5U42uFCTTSMggrjtESbekRNB1/d/AvgTDTBItQzi8VN6/cx5Lj\ns2xkN0Xl0NcLft7GkGCZjBEfgd3ZK6OgI7gC3DUW2tMUhwzaKlMmQJVKlAIIfPD8GEGgqAhNsSfP\nYCHgqC+43b8UReFfPv+M97ShIj2sSpTAiiKoxpIGTYCtQ36CIYGQEeIkURb4VgQnkGgsAsvFQnH8\n8gv580+msW3+HcQYoEKKtZscTrzdsGtfA68+5jLx7TR1Cckxx/2Ede9fyxtblmB9tJE9i5eCVebI\ndBx7Qgtb5W5WPtbDbc0bsZIbGOv2ceADyBLBF1CxBFgWE9JJknGXgoRiTmPyOWKXdPCNwd9ytPME\nmzsc8v0e/VnJ/h21XNA8l1887XG4aOAH1zXjtJT58ZI+blrroA/mmXpZK15Whb1h3md9Wxt77AIF\nCam6FHu29GDiQ5RKPvbaJGv7DEV/H/PmTOGmsY2ceYfDxZfP57RUMzoZoCOa3K9z1Ix2OHPOXDa+\nOIBf7Ea7HlbRpmQMtq+ZPMUh9X4Lmb8KTrp2C5HFAzi2pKQ1yURiJPkogidCivKgAr/bR7W3k0gP\n8OqqAsW3mti4xfDm7jIemmQgsSqKYiTU3wtpE4s7vNXdS0xGsWUMIzWBH5DLZviwT/PDIRe7YlMs\nFBmTTqOMT6A8Vm/o5gnX5biFvdy4pINVAwZPa+ad3MDkFFxyTTezZkDgjgOZRVk+RXxKEmTSRmJT\nsovcc80c7rrteW5/5kyk5WLHF6NKOZTfzacH8+y0n6fwsGZ1+lLqG8YSd45ifdtMNrfdTK7rPmbO\nW87zZ7p45Rx+b4mS0QQWBAiCwAtHgMJFRgkzHxAYAzqAQAeh27dsYyyNIxU7OnyKb4xm5u/LTC74\n7O+GbJ9L8d0BPE/Sk/WxA5t4tJpYYFPUNjE0plJBFcBOJVG6k55CAT9foCKq8fwYuYMSNaT5TEXJ\n/x/s9C8CWTkKWAdUA8PAk8PDw49UVVU5wEZgNPABcMbw8HBphPD8CDAN+A9g3vDw8K//tzcZ/hwT\naEzgI4TG0RotfGytkSjKwkea0GjjWRJEHmUlMDqBtAQGB4coRkb57R1b6Fs0F+n3UpRNXHHHVI5c\nnCBvPH7zgsa5Yyc6up3YPJsP/nk3dNey8Ko0P8sW8KhQKhjGjqvjvImzGTurntif48i6G7hn7zpm\ne1cx+dsOsqCw8jkOSkFEg45181xFUtIFAm0xLzmeZHwGA3+dRuMRa9jx0znk9w2RmbKMSxqb2Ltl\nA1+3BLWzWzAlgwCuX9FKTczBNS6DySy2LZCupHNPB946jXDAiUBc53j8yQ2oZIYfeCnGxyxun38j\nM689iZtbPU6qTnJOSx3CT9KpLIwyWEoxyriUhgosmzeZS/NnU5qdQaZa8MmQlHlqErXs9MvsKHgI\nx+KdV1L84Z08ZyxspyadJpUQUNYYbYh4kqAa4kpRNBBN2thRw+JKiYenZLBa81xx1hAP75GUfY8A\nF0sVUZbNd5OSYj5LyfgEIoK2o5QoE7c1N2/12Kw1kx2BsiR5V8GAYP8uwxnfDVj2s16+MVqycU2e\nPt+hPmGYnAmYPsfmt2OaWH6ozbG9W5k8Q/KDM6O81GSTu9HGcwJSjgs6yrV3vMiJ0y3e3d/EXRe4\njJ10FONnLMT3shxou5INWxez8PIpjB1nYzsX0tw0ho1rjuSH59Zz8iWH8vVvLWZVS5YxfoKcJ9BS\nUMIjKiCeSFCTbqCmYTz1qQYS8QSHRQUISWAkB8s+B7Jd9PR30dfRS393nrI2GGPjTtYsfShD3O3h\nhDrNYFaQdyXFAhjPQvsVdLmAQRJDok1IKtOOxMsNkGOAN3u70LkAbXn8oazpe30D/aUNbA7W4LPz\nCxeF/xKyUlVVdSRw5PDw8K+rqqps4G2gFZhHiF67r6qq6kYgNjw8fENVVdU0YOFIUfgu8Mjw8PB3\n/3f3OP4fGoZ/XfcHHCeJkHFwkth2GqJpLFEPVpKyXY9lOSgSBNKmohPY2ORxsYiCcLhm0gfUPi1J\nWR14uoH4kgf4zs3TKeZL1DfsY/FHe2gUS9i23WfrsQnuXOOjdBff+vQqNq/pYGyjonZ0CzX1Wa6/\nfBGy3MbiOecx55EPOfWyNxg7Dl48aQXxdDf93Vk87eNpSUkDJgeeTdHXVPw81575PZ5edhQ72tpI\nSM2Y1vOJpJu54AcPcOnlDo+vuowSGt3Tg44JhEhQGcwzeXwcKV16/CjFfIHO7Vt4pruAJw2uI7mr\nNcMrazxmtl7IhAzcvGIRnrLZs28rJ5y3kNunJ3ATSfyyTVFrbKuaTCbJ5EycRtfwwV8mc/1NDax8\n9FZSdUmE7ZDrLuDlc3T2buCALID2KCIYm0mwfyDJ7Tfk8AoB2jIwFKCsIjk/YH9Wk8t7BIHBsQWL\nz7mI0o4UO3M9HBiCHz80ntqGImM+b8FNtnF0MoMUHqVBH88zvF/W9HlgaYe4CPCRUPGplCUBUJEO\n5YLmQF+BtT/eEuqDTJG+QgHlSWbVCppnOGjP4tp7L+OuB5Ns7cwT/3YnPbkhHl7RgGzw0T6IEfHV\nKAecRIITxzq8/tBlTD57Buteepo9u9tZdEErR5yymDNObmD6lAyRjGZqg0NzeiL/espKLrnlbkp+\nhh3/lsExULBjyMQ4asY1M3XqyZzU1EgqE8epNth2LESiAUqHAFxtKVRZUvLhnb4sm7dvZcf2bkod\n+8h5hrfWSqre38KEc3zQhnzeJfcu7O2K4xei4JURZbB0NUJLdEVjAsmgp8n6WfYXhninx6N/QJH3\nCnT2alY/W8QPYiTHVZN/9cr/HsjK8PDwh4SUZoaHh1VVVdW7QAI4jRDTBvAM8EvghpHr64bDarO3\nqqrq8KqqqiNHvud/uj7ncxRlHOWDJYmYBNIYpC4TSIVlVZCmHq3DGCwhXQwSX0gcwjgtHdg4p91A\nOjmWvYn5HHP3mxy/cgV5awg33Ye12eJXV/eR64bnehx2fnMiRbcbqQ0zj0qzaMImHnvuHk4cczHT\nthrWz27hptkFls4wzFsa41+afsfeji5ynsO0KXPJDSVQ+V6Uho9NkUN1qFQrSUNOu/zum99j/Sst\nLLtlA7cvh1HVrZy7sI2VL83l1Olpclv7mDrR4f5dBX6gJ4JUEMABDyIxC6kD8sUs9GqkE+YsaCTX\nb8jS47hcetVojmiNUCkolOXT3NDIvfMX8cwmOKbRY3Ktw29zOb7TkMQrB/TlDZgCm9eswqgsi+Z1\nMLY5QabOob7lOnRtHbJ8HTVRRWmoC6E99nRnOa8hw113ZLj/jiF8XQwDTlUc1/ZIOQahrZH/Ipf6\niUn29NvUe5pX16b402GnM+FyxaGf/JV/OriCUnEJB22NMAY7LknGUijl4QsL7URJalA6CjGJwhB3\nJCYpGJ8ZTWr1AH42D4UoU9dorETA+FqHmoxLyTL0dfeRzRepj8PPXo2x/B5NzQyJVwKpo8QkyIik\n0Y1zTCbB+LrpPNjWxUWLH8DNzCOXHeKpBz1278nzi8ZuDqsVjMpEeWOSzZ1PNLFgZjf1K7uxqydi\nHB+qE5zWNJsJE1ZxfFOKN+ZAsxNyIl3tA31oElREuMGVhGIhh9ZQ1JrA0YxtzlCTj7N/Z4qXt2zg\n+5eUqfuolqDQTSJdQghIflvT2VYhHoMuJg8AACAASURBVIR2Jm0nUEqgChppbIyWHGGX2drrI5Ac\nU5PgrR6PfLsNWhJoiXJcGiXAlf/Vdv9iReHv10gsXAPwK6D67zb6nwjbCwgLxh/+7mN/HLn2/yoK\nf5/78M1DEyiVR9vpMCdSx9DCRcgYBoUKhpDSh6jAaBe0QUiJrW1KQuDaEjzBWc4tLFNP871L/4Eb\n192KjwGZ5O07O/nV0Q+AzhJon8d+fjJFmSZWrZh1usWxiSEevrMBu0Wy6Gc7yfxrgSNTH/Le0x4X\nPtTP0tx4vjqpge9f8ADfW7iG5q+/waDfR0llKPpd+KrMYGBRJDSi2LLEJZfdgi7PpfkOw+ruGNev\n6mHqZ1fxnYk5yHcg+3wGy5K9JsEL28uckIgidcAo28E28PDTW1l7TTs3LYgSsxRBxEVZhrgtGMTn\n5a5urm2dTlkWMHlBvpDjZ/fM58TWPLJ7H4M9Bcp5jU1lJP3ZZm27z3lXNRBP3Mjmf7uKzTs6qOgs\ndmMtz6x/hWsfWkAMqIgAUdL0dK9i1TrDEdEhfnih5IWfugRRj56oICYsYkoQETFKSpEva3J//Roz\nbnieeye38KdPv8W8h3qpabgTTMA5Z0yj+9a7eehSONIFHMnR0vBewqWoK2EwT7UNhRCVLqSFkBrX\nkQQJyYRoEhpg2ZKtrNgyQH1GsrDWwtEJRCrB/vYBDrRrRh00xPOasZM02hgmZMbhJT1koEnJgDFJ\nizE1TUxoSKO15vZnC+zJbmBsUzeFXJSSNuTzZfrzBkmeY5wEP3vjTlZtGIeevIUd/57Ft2yaG2Bs\nIxyXVNREJZZRaL+E7eQpmTAYpyQrKO2jyzY5pSh5Prrs8T5lBksWXtnHK+WwlUsqmaC/0MXwcUnO\nbahj6W3bqakr48oocbdMSRJmUxYUQgu01BiioAwlC0ooHt/ai8Kmr7cOWZ1h8dmz2bv3bE7LfIId\n/f8AslJVVXUYYUTc1cPDw+Xw6CBcw8PDw1VVVf9HsMe/z31oiHxnGGEAH0snEZRQIg+Bi6aMdB10\nUEaIOEJAhTJWAFpU0NLB8wWuHaVwy1cYyr/Niq4p6OoAyprGpGB66x1cM/FFNnb49HT14B/+HHc6\nNo7cw/gDMKuhk4curGX/3d181bP5821ns/L9BzmjOcEjTU8y9vQ+Fk05hv9wylxzeTdXfn4zZ85p\nYrc/jppEOzLWh5cL8fN+GbwsFGWMw77VzgnRNaiBq5g6dyIVGTA+HmfsvG7e3DaDVw8O4Xsxzh2f\nYVR2iEpc4iQ9ihWQUnB160Q+KRTJuQ4lEZB0bIRlM8Y39OezxG6dzqBfAB/681nGe71MaExR7hxg\nVERCj42nh3BjMSwhsW2HvoE8iheZtuBsuGwZ255eyDu7u4h951g+P+ZzDo0JvpU6EonFKepI3uw/\nlmls4ZQJdcTH1ZL7wCepDEVpk7QD8toADq7j8OJLe3j0919n8C+/5Pd3fUKkdhsWZRAWa154is7X\nXb76zOM4n90OlW6UsDkiCVo7fOKPqGuk5KAQWLqCJQVaSrTvI2xDPB2j/vwk+/urMeWA0kSBH766\nEAjF/Us7uCnhgKnGkdUEOoVbdkjWxZEyyqFBBDvqUCoM0SN78fCYaRvcjM3Bssf+bI5ZDQ1oW3Bc\nUmEnoUaDyz66Vszntns0nbkANynwcwPs7UrzvgdjqqE2UYspDJH3FVEsPEughUGZgNyQ4sO+bvr6\nutFC0FOdQPmKvKcpbskjhCbnCaSGPVmP+LgkC85tZt7VfcRjHqWijQ5sTNmglYf2BbpcwlSGgGqM\n73Bm4iwkz7Oto8yEulpeWb2C9NGdvPBSFcYy5PIDTHr0i+3NL1QUqqqqLMKCsH54ePhvuZFDf2sL\nRs4dvJHrBeCov/v4N0eu/a/X51XgaXzpoYyH7TpETPz/oe59w+Qq6rzvD1K127UX1dJnN2eWbplG\neta0m4nMSCaSWROWRAIkgUSDQuSPiPyVxYBCFiOIAosIqCAgIIjIH0M2RAkGMGgSSNgJm4nOsJlo\nD9uNOeOedudkPY2n3LuarVp4Xpzcz3M/1/U8u764r/vSetVX9/TV58VU1a9+9f1+P3jTQeoEb3pA\n/BbkAEJ00fRgVIowAYFISUOF7Tges0tR2oIPcrFGsYfTepp866HdfDZq88qGc3jwGUV2+/OkQUS5\nMo9573ySfrWXyajGNY9s5d4PVPjaTSfxTzdDO7N8e0eT9cUtjL+8gtnBVbzxPQhPrxIULP2VkNO+\nojjy9IW8Uh0hGWvQiRUH1DQtI/mPN+BAfDtrXziO5e98mYXDfbR2PcL2iSdY/NpCDivVmWGmWVyC\nqbqk3YrovXSEx84POKIeIK3BZo5avQ+rbE5ukgIbdkhxCGuxE6Mo7RlPLLvGthHGCapUZ+lglePm\njNFutRhcMYj1udhmz46Isy5bhrajzBxcykZxEsfUt/CjvWP8eOsWkm2KXlXCKw8Fz560QJp0eGU0\n5V3zJP/4aELis7ybLiVWa9peMnB6mb/40ydZc/MGZq74IEoN4pA4xoAIXMz8RRGmE7Lsr97kH2be\ni5+8CVeyKGN458FbjtfJXX0+BS8UwiuMz9WuPlQsXlTi0PeE7NgwxsZn4HZTRYsA6aExZvnJjjpn\nfOMzpJ1xxkdDbOrp13XmD6zgO4vO5bV2yo6eLbTSLfwsTXggCjGJY89+Qzlw6KWaEi0GRJlqJdc4\nXHjLGk75UMShh3tm1oaozw4Jwi6ZGmSfCXgpmqbgShxtPCGGKZFLnds+Dxn+VTTBsxvW8dglI+h6\nldLJs+mmjolmimm1UQWFYx4lr3EKNo6l9P9ZlZNP6XD96iZCRfgEtBN5LDw6p1xnhrbMwBWxKuTU\nuXW+JzXf+eqD3H/rV5g5exGIProIMqOAf/4vp+H/HP8tS/LgbcK3gJ+/9dZbX/1fPnoa+NjB1x8D\nNv0v7597SD6OA37zX/UTAN56602UtEgP2AyTRHSzCJs0Eb5NwTXBp3ibIskwTOc8AgROKgJr6YgI\nrMagMIVcQ6CF4GtxnSO3/BUXb13NyJlT/OX3/wTnhkDUCMM6harEEtAfetrNHqSoc0DWCYueWo8g\nVDC6v81xw7OZ2rSCZ2+tc+lVg4SBZMmiOvu+vpo1Q+dy5cJbOXHBF9FHzQXbh8pCcJrevoCNGyyz\nzvscrXgrtWqAq1kato+VdcGnBiQdn1tdB2oht15VJ5wd0msEhIoj+qocFgje31+ltxwySyuqZU1d\n13l5dIyvPTTKxiealJzlhPM28PxETDUM0KTUgoBfGssMXcS7jJf3tJhKUjau28po0iAUhpnzVpGk\ng/TPXUBswe53tFJDezom8oLEdDFO8ezEBJWqg0DgRO4BFipDK0lBwAWrhpl48Rain54AbhTr1kG6\nFh8djm/8B7bxzyRjv8H89FC+++AoKz5VRPUuRXYySlJR1JpSEHJ4ucYMEaDDABEoZAG0LoKDTmLo\nLR/FzOGA8aYmQWKtIkkA6+j6APQCLrh4NkmaIVSX5YOC+1ffy5/O/ATn3vs6f/v827j20Zf455+1\nOLZvmPlzF9DaH3Ha7BplJdm+o8HkaMrUeISNBdYJHrjfc8wJnra1VIuWmsp7POCw3RhjdzDVfoof\nmzEmkwbtToOo08RETX42NsJjjz7Ch49bywf//Ba+evlTbF/3Irs2jzC+Y5SJvRFRBHQbWG3RShNo\nxYTtoJcIohmz8Wkdm2mQipLKBX2SAlIpJB2sjZlKmizs7efpL1zD2vNPpxxW6Trw7AdpUPp/r6Lx\nr4BzgL2HHHLI+MH31gK3AH9/yCGHfAKIyEGzAM+S3zw0ya8kP/7f/cB/HnJQt21TEA7hY4yoo4+q\ngN0PokBBtYHXMHYAh0LJFKc00lhKhT4KQtP1uaBTdUMKFPBovHC0uv10A88+sx+HxlRAqDalapfr\nf2VRNqAVxbnyrq/KWRcs4H43wXohUWGRz359K899YRVGKZ54YzMnzJrNCctq3PjJQZZU+/A9jqXh\nUi4492b8K2+HvX/FK1u3ER++no/efjuTLzaoBorvth/lqtICxmxIv0ogahGHBUQXCIBUUqtZ6oOz\neX5Hk44VHGE6VCt5Y+4EBRjDu/wg5ULIrd8e55V10xzZ52FXyOebVRqyiBnfix5QTLYtdkfCpPkM\ngbPsGmlw302n0905woG9Gl8bYuFlH+Y5azllYBHj6QSf/fr9+G4FUchjvYpFw5S1JJGjkTYoqT4M\njo6yaKfoeEcQBBwTwC/u+QZDz3yFy895nVDtJWkdSTL9Eq3mHqLWrzmQQhy/yL69d/OjR7ZyxhVj\nXDni+ct+kMpQLKg8lqjHM8MKXneKxJicSlUsgFMYAm64/SSWfzRm4/cj7v1+izUflJQXzGWGSdmx\n4Uk+fls/py6Bmkg57eN38duFF/ODB19l4PwFzHcg74M/HbuDX7x2HQ9+/TPsermBos3OMYH1iiUL\nFvEjV6P2lR4m0oz1f+zZ2YioV3ej6p7ENiFVtJtAViTaAp1U0JaSx9U8VtYNHWtoRdNs29Jm/f3r\neHyqBYGmGAYkzQ5eaGqVQcKhfmrvLhKGZY4Ol6FCBRRQvpO7ZyPL9p2KkgYoYlOHMR5pBAJNhKSj\noNQOkdV+Fq8aohoG4NsIejDSY2mSWKDwO60Jv9Ptw0vAIf8/Hy/6//j7t4DLfrefz8chbwKZx+Cx\nqUMXU7wYJZ2uo7J56J4A5ccQCghHUfRjrcHgka6KJUI6KOkePAqjUrpOYuhgZYjwPTkHUMzGFGMk\nbaCCrTRo76ug44h4ybVMPFKlbLfxg5nr2PXvT3LZC55b3rGQtafeTuuqRVzadzqlV2ez+o07Ofl5\n+PGQZUNRMPcXq5gfXcPifmibBHTMwhWeY858kmMWXsvaL15F0Fflin/7Fa3dn6FYW8fOTkbSFtz+\nKWBLm2IQUHIR/SsWsdNpdDjMLPsM1cEaFkHYViSRZX59IZ+/6Sn++pku/YV5nHpxQC1qM6lCPnjB\nnVz+4ZOYiBvc/cSHmbhlB2K4zhnD1xIe8zmuuHE+Jx1IKcWe+77wFF/7u3MZn36GPXyZd8w7lx/c\nPcWXrzuKM68o89hYSjuVmBR0N49sf2HUIHDQKOLxdO1Bg64yXHD7U7zv8QW8cep1bHzhz+nVp7Dn\nlqd49s9KvO/Fo3n6twn/sM2xc2wGd297F+/YsZgk6edZFfIKIUdPNKj3CygHVBEkpsORRvKKAU0R\nJ8AHAiUCzj53FfduHuHDl93CxXdGfOIexwlDGUr3YLox3NvguWHLfAz3tdbwRu/X8OHtKHxupPCO\nslhI64g7Wb/hHtyR56GOqlE5/F62PHMpz/76cp7//ksocSZ4sCg4tM1n/maCD/31DXzxRsMFy57h\nE3cswdm9KGnpYEhTT1mX2Tl3FdVkA3GWcMAKdF+H+753PkYIvvRByT3fOYcTZsHEby0HmoBtQxBR\nCEJCHKpURHuBCD2L+yz+nDonDo0xIQ2IkECexAnD5zN/aAHL/RtMTEc8OzJCwWfUgioGg3PQsk2i\npiAxik7L/1dT8P81/tvjw/+JkcucLUbmD+4tOBNDGmNMA2MnMDbGighnE7xJ8dbmMFRp0GQUyDC4\ng2aUgEyC8lUCA16lhFoTBgFVXaHcUycN63z34ylnV/Zzwchq7m2eDVWF6Klx2ZWKIz83xK33l9g7\nc4RLPjvMO9/3CO8941FImswQApvmSHFtLW//wFbSt93D13+9hA9c+Sgm9bStYE5tBUkjRb9rDUef\nsAI01OYO0jv3XGoLh1m8fJDRpuFLXxkhbm9GDeZZe422pW0idH1e/txUEGlyEBjS5iOnz2beon7C\nahsyjyyX+cmWBvPqs3n2qyt4+qFREhwPPpJy4tAQncjS2h2zsBYysTNi+cIyN39hAYuHq6w8uY43\nLd7XI/jMXb9h/d5pNn43YUn/XHRxGroJCRYlIe0RID0WS9lBIhVelEA5StIwHo+xcesWJsfupBM1\nSBqCJFMQ9mNtGZKQpFVlsl3BJv3gPJt2t9nT8VSDoxidSHHGY4zBGEjSDLygIEFrR0l4ZqgYoSwr\nFw1z5sX9dASM7k9I6NLuTjOZtplMPVHb0kosD+xOuOGsAu2JR7ACHA5LvqEksWBfy1PrUZx10hC3\n3X49qy/dTTq6DuUEhm04RhFEiCLcebNm2Zw/58nn/4m3953Iu9+xlp43P8i84RVgKigj6JgUayJS\nC77t6e14gkwh+wRBJaEcaJKfNti5a4xOMyG2ERN+msykkMZY6zEmw9uUrsxynmTLYVuD+GAV9cG/\nY83qx7jxnC8zq74UXZlJSZWYaUMq5CGGJs0wpoVNEtoktEVEkv6BRbzztrfhhaSEOKhHz00kUhiQ\nDbw9HqcChNWUZAkvDUJY0AkCTceFCAxaJuCKiGKLwFaBGKEDEIIuHpQDpXHGsLBaY/7xM1gel7jo\n609w8w/rUOwF2yawE+yMDVTg8OYY63xKe6TKyp/GbLx5kLv+6ElSUSJpeHZGDaZali8c38Mlnxrg\n7EGNaUe0hWVWvcB3bvomH7vpi6TdIQbqW1GukqvrgIkkYbwFH/2jCabOGUAbeG00xaRQQFGuKjrd\nDoUSlIMSaQg4x8yyIggDvBrDS0tk6nx9ww6ee/wiytUiJgj43q1w481LuXnoeFphxMMXhGwfa/D5\nkmXjpgZX/u0woiAItaNUiQjDBTzxXEgUWUYbMfE6w9JzZ9Mg4uV4mhIVKqUi3VEDmaPlQWUhrijQ\noaK/EtCra+Azsmg7Rv8ttdMfZc1Vu+BfFjH+r3dy498/zGN/DVkT2gC+QDVos333CF+yRcb3W3i0\ni8OhNgd4a9nYFahCh3JRUNaa31YCbKBJjeXKG85l++Yx5sl1XH1LSq2WEvZVeLyYYVG0Y89PWoLt\n9+7i+Bd/ygnXHQw3FSlGCmw8wj833mRmf0AY9LBr11M8fsomWv/8EEPL/h0ChyxdjC3W8dkPsT/9\nFwp9fYTVFSw/v4p/9Se4qRJ/suNa5OvPM7DoPLZtHsW4FEUILsnRcrKAEJqOrZKplLQrELFBqyYd\nnyAcmMCi0EiRYowHYQltQODBiAFEOMzlq85l5bKVhGoXqaviowxLDMbS8QklJWnJKJ/Utk2HFtpq\nMDCV/e5T/feiUjiEQ/IcS6uQeIz1GCewPsOmbez0GDKbBhMBbZSbRBOhTIb3BqWivCvrPKFPKVkL\nahqvJQhDV0mcglQonFIUSyVO4yH6009y2Bd2sT7+JLViEa3qBDLEBgHoQbQI6A+PQqo+gkrAyVdO\nUF04xNtuf5gjv/00f7llO++851dsOO8nHKhdw4Hi+djqap7NahAJbnuoRZwto2QU8+wOfHOU/oqj\nIyxtGxPohN6Zg7z260+yeEiyfVdKZA2qKBkYqGCTBoUCBE5ihKReK6ICifIgOtMkbdDlgLW3b+DM\nVcOcMa9Oa0fKjcMhnZFtnLqsSK0vpL8H9OxB7v7RK1QHZyOo069iTDJBrw4wu/cyUysmN93KgbSH\nThrgY8XOO1KqDHLJskWsXDFIWQS0GyVSqw5yFQVBCDMrUB3ULKxI+sM+pJhG9ChmLnqCxRctY+UV\nV3HHjz/AH/X+D97ZexVn/u0QQkCoEtI0JcWwsdnEqAwrUqoqIQya9PbEVHVCqQJGK1oJbNrbYvvu\nJgeyhDOXzuO5TQv50b+ew+LLBnFHhcxa0Y/XFpOlOOFoU2Si1aTdfhHI8oQj4xEqYfu6TzM5Okal\nx9JOttKKYzbe+A3WX7mU5+/4JROPHIcZ+xCiuZZkx2KMXIWsrkaQoDxoFKp6Oi/80xoGj+6y8n2v\n8uOX76VrJLgiRiqc1oiwgghDRE0hK4pJ28EbTSexkKYYmzKVxLSnE0wnRafT2EThfIZ1go995jIe\nWv8Vlqw6G2MtrTSlEzVJ0gk6+w3t1BA3YzpJBHFC18QcSDJ8PMZ4Osp4Okqajv83s/D/Gb8XlcKb\nb+X5Kg5QCJz1GGFwdAltCTopqezLk5KEotRTQvopHPMpaEseuB/hfR+GDFyOBDceVFChoDLy7FCF\nJENYxfr2Is743Fe45qm76Ki9uNIgRevIwjTXQzAJNsAXYwrTIQUC9o0J3n3vTRz/Wc3Casis2Z41\nTw6zZGvCe9b8kCUrljGw7CTOqMKB9du475rTCYSgf7aiVF6E76lTyBwlQoRvIVQtn+S1rTw/4Yia\nKbLoEQgqRYdNJVYJavWAciKY0Hmc11RiUUmHXhmwvVXg6qf6ePCak/DOsy+C1rmLOGtplXm1gPAk\ngVXwWppwImOsXTXECdeWCBf04OO9qKID6RkfWccPbljFiRtnUFQOHShKFU1rb8D8AN6/POBXFrZF\nKTb1vGYTZhwlmVUtI3sEx5Ylt18T8ovhOr1XHc/OkXN48PuH8fY/G+HCDz3G8lVfYc7su/jHn5zO\n8g8tY9PfreOuT0d5F185yoFCK4lXApxGSxDSE1YdhUCitEBVJEniMNNtkkRQChRWwpprBnl+a8TL\nL4X84KU8sTepFJlKilQNtJwgSROkeT3XOzsL2Rbe+fZ+HvtZnOPasMxDsWuH5cZVp1F7Z5WLP5Rx\n3o0zmLfoGHaNHMfCj+5E2DEcQyATYAglq9T6V/Ot597P4T+cz/pX9zH56Sq97/8G2lToCOgEMViH\nlYpEWdIsI9QhoZCknQQlNAKPISY1ASIEVUzxNiAcGmLtqx8nUAFTUQTTAnwCXtFuTjGxeQflSkrX\nJjSaMQNphUQVSEnwxmJdQNsGGOt+5/n4e7Eo8NZb2Aywlk5RIFWBwILwDqM6mKxDKJoYFhAoRUe1\nUUToYBzsAII+pCrisXiVc8usTdA6pEsT6fpQwgEZxsvc0lpdwMMojPoyM1SLdpAi6KOIJ5MdSqZK\nR/ZTUBbb08FN1ymoQYxo8NG1Y2AjfqMMFz19KaA44iNP0fSfpvDbfpLRB7nx/FWcsnkhnaIkiuGU\nOecxrkI+e+1qzpur6DTzykdkko6p0C2fTK0zmymaiEQxPpLQaAgurdYRQKugkM4gjaNkBL5cZqDe\nw7eeiLG1kO26yspP7WJPmtIv68yr5SGrZeVJUs+mzWOcdVqD6uv7GFUTbEsGufozL3LlheOcffxC\nrrvpIS74W8+fvBpzx6ObueLOLSRYKmGF51/U3P3liI0Fw84x8O0SBeCSv6mjsRD20F+GgaLglSBg\n54jj1DUDfOrRz6G7gp93juaBd6zmwa8dx6xVy5lqpJiv3k/viEabFCqSXq3wWlGSGp8dDPX3UNYB\nM8JqbhCzFtsWHBAxieyBgqMjLcYagh4ozw6w0ykm6eDxtHFYb8GuwmYtXNrFyx668QjPP3UMR5aO\n5Il/24y3E2hVJ1WWdtPy1c1jqBcb1D8yynHvGebUYxvc2ncho1sD5i14F9X6GdCzDEpL8RyPYCEL\nhs5h6THD4BtcftFJbNoRUlvkiJoRWhqsLGGcpWSLgCFJPGFZUdV5mJA5SGXoWIlJNSVvmTWnyhW3\nXcaZdhuTSZvEG8JU4ZwhsJrUNLBqmiTaT2tijMSkzP87S9dYPJ5xB94nuCwg8X9gGY0AXhYQUuKw\nSDxC5lR65xyqqDE2QmQNTFKkIDUCjUNQrMRAgLMhnoSCLuNVjBISfELXQ4EYr3owKszhMKFFmSLW\nZnR0AIUq2mX4YgdjE5SoYsNelN0FooOyFhNYho6q8K2LzuHY0x5lZp/lva8O8e9Lt+ArfcyrO267\nbi6Pr1zIyUuHeNdFMc/8+ZFUkpeAPoLhazizXuV710yjHSTxKEpadLEPwgoiyujSgAS6LsI4Rf/C\nOolqkBiPrPRTLk3T7kBLSlbOrrNtR5OxY9cgnSTZ8XHOW9di18/HaBd30OIiJqemCZdHnBDWmBlo\nTr4n4ex//BzPzlOM7r2H++a3efbob+PdvTy7N0K8sIt5s1ezo72LbVGLZLdAqhY6FMw8qg+RWZJ2\nyoLhQW776hBzqpJW5AgFzPMOu7/ByxtW8XU7wrEfepS7vz2N22/R6pus/SXs2r6ZW68/gw+d8gUm\nog71qiKTiqoqonWIzb3ZSEqUhMIaQ1koeisBgci5jNThGBvws7aiJQ2uI5hqQDszYC1Yi8ehpMjV\nizaPVLfe0mk+jLO30PH3cO/n/5Vj544wVBU83yqBqFPVkGQWHRm8UlhreTgao7pmL2G9yBl/PczF\nZ8Usv/gwjhs+mlrtGIo9J0PpbKS8nvmDV5O4LVhbZeGyCnOqH6Bgfs2UfQjvukiV5WAiPJPWUOr2\nUyuECGnpFUW8zZhUCUeTUQ0GueSKi7jwpJAomSSxXVQSEakAZWEqixBJDMoyNdEibk3wk637uSEa\nRGlDAlgr6PgizkS03R9Y8hJvA+EUHWkpAUiHlxLjNMJ6KHpSZ9C0yGSNrihQpohXimx6H1LNQRUV\nIgzoWgE+yKPbRQz0HbzVUOSA+xCN5kCQQibotR7jQkRxgi41ECkqSomLllJYxbcTUBWcbLBmleav\nRh/nw7cM4uUiLhz8Fx64/TQ611/LBjVG4ZVVXLiizDWbt7D40jFOPKrFKw+sJrIN7npggDot5ixQ\njO+YQBuDqq2gLS0i3o1obSEdaeGcRh3VBziiqZiPfWyEq6/qY+nSJtZ6RicMZ8xbiCTjuc0Rjw0v\noBtIete1SS2sPHkVk8T0bwxpxBnf3rqXs66q8b56wLaxNZw6+3bmD1dYUq9w13dTRsciLr/uTtYu\nrfDAv93JVU+diB7cTfj5OlUFqUxyu+7+lHYb+ofqjD+2iKsPOMLAI4IAKSwuiJnc0uIy+wyXTCh+\ntCNj1xbPvrGYOLGsnyiy/dsd/uGFTWy8Z4zPnuWYSiyhKqJLCoqKsCIoaCg7hZAKaRUDukhY1GBT\nXOYxBYFSAX9cNvyjBZv6/NwpC6AEXmlKHrA5is1aR0cFdJLNjI5eTzdLwDT4p50/JPqN5epli1i7\nbh0TjWnm1zXWK0yqcIFhyhu00Ys6KQAAGoNJREFULdLZrymnGTffvptybYSBvyiz+L37ue6jv2Zo\nwRsMDL9JKZjJwI0X8Bu1n53tGJVW+YdXxxn6wDfZ852QQHdznqwyOCdxCPalMWG1TqAtSnYoFSV2\nb0YxrPGR5Sdxy4PnEyVtWmkbnTk6XU1JGBIrMPEExqQQp0w29vJKcz97GhG7drSo9HdomYyCCEBW\nGM8sb6T2d24g/l4sCoe+9SYCRcF28AefyHmLlgVc4LHCIT2YJMb7CMQgRil0IUQE4OweFPNQYQnn\noUgdJ1xuadYx2vXgZAw2pCtSJIKCUGhVxEuDB/BVuowgfBUfJoQYurYGfhRcghYVrrgn5Yc3jvGP\nL9S59LJB9IztvOcz/85jH1nJe479Og+8eR7J0XV65zfYtMWw/J1THHfhSv5Gp1y9egGqAO1Ohz3b\nmsyZvwg6ezFpAzf6DMkz23juxZhjzl9FICCVAUoHXHjDXNZaQbytwfq0yyW74engTB7+xjhXN+EH\nT59LZi3dzLJr0wgvxzHPPPUMcdqE0NNtSZIHFZWlAbXDJJM/TxiYrehvSmaFBWqDNU69PqLMnUxa\ng/DDtJKI9ZsjZs1VeGFJYsv2zBPOLTPvV+dyzxdDnh8r0ilYRDlDCQjalvFaCTsqaDSnMe0E4yOs\nFRywUNgL39q4mS/eeR1n9M3DJwHaROhQcUSgkVpQQFJTjrKWaKWRXlIIBNoapJKgA6wsIFJDZEs4\nG+X5naGgJCUFFB3rUZUqzlu887StIcWzacNNRHuPQekzmNz8BNfe9w6IY84+vUrtzEHajRIMKjyS\nhBKaLjYJEWGOUJm0BUqZJc2KbGw00OdNMHNoOfNOrPORU/4Hc+b+C8+v87zno5fTiScgqKCPNLzj\nsLdz9odXsenFb6KMyjMGZSEPY3Id9sUJtcFBFE28FNT6AsK+CotXfRJjU+I4ot1W9BJgMktEg66V\nuCRFJYZ2GjE+Mc6+dS1aTcMPnox53ycDptIUIRRhsQ8yR9r53XUKvxeLwiEcSlcmaOnyUEsXgs4o\neHBWoKzA2jxM1FtD1zYwZpCCUihpKGkB7EIlZyNUbuWVvorGYoynoD2o8GBpqXLQp/eARVmBKXaQ\nNkDreo7ecmW8ckRqP14ECFkGQrphH8uSyzl7OGLJR35GaGOsT7i+GTN5L3zrwRVMPryaBX/0GT5x\n1w6mrrmZh689hwvFDmpZg8RVaDeakKRwYDdEE0BKp5ny93fvhUULqM4ewlqN1lUwcKQWzKiG/P1D\ncO/+adZeUeWsrQ3uHtnM4ScNszDNkMpTCSxJvcKu748hRT+16lG8LPfT26PwAkoqYOVJCxA9W0hs\ni2odtOphzoIylw8GtJMypy0ytBLYF1uSHQkP/jCi4A1VLbjnw4v4xeXrWXx8HfHiNGXpKRcFvgsa\nQankqfZXiFWRN7xnU5aCc4gK6BScyVh//wiXX3YHT2+8gtsWrOB7TwqEAi0sFQUz0KggQClJGY8Q\nGikNQuc8UFBouihqDNk2QRpSCj0l6zlgNIkwGJVHr2XTnikjmJFZnhvZyn1XLeO4SoFK6Dn5gbfx\nxe9u4dMf7Cesl7juwgV8//HVvLJnGaPRQ1RtnTIOFVo6KXidIooKY8BYg/IpBSfZ8/MxLr5yhDB8\nCB0IUAHPPhmxayxh/mCV5M07mRecw+qFdQ5f4ikKnfMXpAOnEKpAZFtsb42xpD6Y98nClOPmlLn8\nti6NRoe2T7GZQVMisQqTGnzisLEhNTGdNOKl3Q02jbxIqHt45rsRv9hheedAkTaQZDGvIzDiDwww\n++Zb5JRpOIjstmA7HKBEwQl8aEBbkDLHd3dSnBrDM4xC4UUZm4KSrbxpUxAIJ1A4UF2ctTm2SyqE\nT5BovA2Q2uM9FBAYmTe2giAk8xV8mhIEFVJXBj+OS+o4GeOCPu5NA5J4BwdMky9eFvIvj1/Evasr\n9P1sKYML4Lrbu3x+Q5vFt9zDfUXDJxZD11jaUyOUZgwypx3g98Z45eilQuxi7PC5fOyGMwGL1nW8\nrhBFL1KtSPaNRRxzTp3nWoaFzVGuFg2i9Blu/NIGZtyk6NdFrHXMuj1AS6jVQ/g3wT4bMB41mFlc\nSseknKUElUAzYS2tRsKsQcmAqVKuBMw8SmHr00zFHS6wg0TXZRAbxhsJU9OOMxqKjYdvoXx8jf5A\n8WzJUBMZZbok0lJXFUqVKpHMw1tRgOyhjMCohJa3tGLLE+t2cP1V9xPa1RyzYBEHXJDHuOsiZS8I\nhQSVERAgcHjnEAKEFEgBTjiUh/4QjokC2grCqqW3XqDTUpggrxDbwuNTi/SKnXsafCz0zJohSKZS\n5rzrm+wbsbSP7+OBex7lvgd2cO6DDUbjP+OXEyMcXRllaE4VVEBZKozJO1hWdYAyHQtCpCRJwlTb\nYB2kScaUtbyWeJJMcaKocsffL+SPr9xAK/4272KMorRgPc6DUpZcDaqZsDFm7zOcMLyQIT1Mb9hD\nkjkSOY33hm7i2eYy6BhM6vDT4E0HlzgORG32bAOlPE5Ykrbg23emtBOQYYjtOpSAGTL3Cv0u4/dj\nUTjkLRAup+zkzgasyq9pugi8Vwhl8EaBAJEJrEjQvgkiwKgAISUimyATvUgvc+6k8HkfwWfgLZoQ\nrwzChwjRwvsSyin8QWNPV6Q4U0SqClQSrA2QsooXIQgNIsCLDlZE6EAx960v8tMTr2P+q2XWrOrw\ntae2MeOYDaQnzObmi4dYX4sIRYL3KSbRGDGM/60gST16dhUjVmHECgbDy/gSAcGDEnwPJuxH6QnK\nqgeDRYZVXhpp0LuoztNPBZzw6ad4LQZdhI6zTDaqWGGZWamghMOYlO1RwkBvxme/0GT99lWc118h\ns0VWDs/mXkZoxymTkeTMpkOHGf2hItCesFqjtysx0ykHbELd9HHpJxz9s1M2/ftWvvtmhVf2rkDI\nfIe2rkAoGoRlTakiCazmCCTfyxMR8RoC5cAq2k6xZy/ce8cEa+58gsJFH+fIJ5toAZUYRI9ESUcZ\nRSgEVkgQecy7dh66DmMlCocKAs6oCWh7egmYWbbMmQMmKfJa4vCRwXnoWoudbjKRBazf6bntkhGe\nGJWUNTywbowzVu7mXR9p8HcfkBR1yrM3W25jhOXPNvjIkjrYkCAMcDKPBDSihTmIjU4yQ9ukRC6X\n0LyadNk02UQLzY9mTnDsB0ro9yvuu3MJG3fY/JiqFMoJJB6HJ3AW0KQ24eGtTzIRLOAVP5tLvKQW\nQCIUE6bJa/sTMB4ZO9wBj00NB5KMyYmITpoitMZYiKYTbv3ICFfuXcRt6wNEAIcK+E+p/rB6Cocc\n+hZKeryDVIJ0edqtzhQdQLi82vd4Cgi6XoLJmFJttO1HJILAC2aIOt7sQ6mFODeNl13wJRAe4yCQ\nDrxCKgHkijmhEgrW4JyiICVGeBwCWQxRxRC6CcKVMf558BUSk2C9RnrBy7snWL8u5u4TEz5w6xY+\n+6V3cfknLJ/6i4s4447L2ZfUkUWHpEpaqBB2BaO7R3luQ4vPPhmz7z83csZJFUQCexojWA1BdeDg\n81qCcpl2t81h9YCpxDI5MkJlsM548kNctoNUzUbbkFJQzLvXRYGgQEjAykd3cMpQSOWb8DffHOPO\nj/dxxGxFpyGY1Vdhz88tulpjuQLhu6Q2oBQUcTagUkggCFBpipYpOgxZgiL1AZtehLWfE5wwNAxp\ng6BdYaZoMCfwJIHApl2ESCH0qLAEgAgVQmXYJN+99zQSHvzGVvZsvJWfRAHH1ixGOUpKQEnjvcgB\nwdKhZL4RaJdbx5UQebvYC+b0NTnGSoyu01sMCKSnbbscYS2/6hGIjsPaFJMVmTKeTQ9FtCJFra9O\nN065654RdLlNJ9VUKwHVKry0TBJZz623jXH3NyIuWFVnhvMUfQGt/if9MfcWtA1EmcAYw+uJYFfb\nYvbmeorJlmW9TGk3Jnh6JD54rAGBQUoBB+3eTuXVgxJ5n2Ffc4THnh9GbaoTqAJTiYDpDknapjsJ\nLkppNy0mcdgswbRj7DSY1JOkEc8+ZJiIPQN3NHm8ErD84jKHz5K44Hefj78fi8Jbh6CFJtdXeIw1\nlLzA2INlo3JIL3Aqw7tcj+AcEMdYGpR8H9ZpwtJeBJaO20JYHEACmU0p4glUglIh1gVILEr15CBb\nW8YbRaAtbe8RxfxKFKNwQYBFI1yAMDWUalIMFDKokEyntP9iKx94OOWbWYsPXjAGhCw85ApKm8Zo\n/MkRnPS9Ksl0H1NdQ8Fb4q37kSYheHeVF15axqmn19i1dZTJdpOwv0JJ1nl6osExtRAt65gc9wNk\nzFDwva1jbG8/xfxFJ7Pxu49QK1bRgSJQMfgKRQEFD90AJpqCL8UJQjm+v2GMu0ZnM/noEL3nraNa\nLbP4wwEXfvgk0uYE47szaoNtTOoQXiMDj0DRsYJCqhFH5YzPKvlEfXrLVrhJYXQVPTbEUGsZH10w\nzVAwCNIQqiq9lTa2bWk7R9t5bFJA2Bx+Yowlblp2NVM+sabFj3f188HFhiRtoa0nrIQYLDnGB7QD\nLxVOGrwzBDisCummhrA7yqtjdQ6r96OUR6gCFHVu79YWb0PG2ynrN7eY2J2gZInURihtSbIuO/em\nDNQFtSBlyXCVs6uKUlBDKsvORpMb7nyRxcuO4uiBKmXRRqsAKx3GGdqJ43Wf8zHbHYdvgjEK33BY\nlTCRONoxGG0QTuNlLs7L4USe/xsNI1UudfYe0QPjP7fYaAU6yNCx5dUoIppM6URg44xOkhKlFlJD\nJ/W4NKUdW7Y9lWBNyIXLaswLJPuqCR1h+d4TmvG+GGaXf6f5+HuxKLz11iHgJbmsyOJ8vqp6UhAa\nm+XcBykkXecoiAL4nOor4hgp9mOYi+k0yL+Z4uQ+vOpF2X5cMUaR8/UCYbE21495EeK9QwCOgAKK\n7sGADLwg1pqQgCStglbIrIKlBSpGIykXK7RcTEF4LBmv/uB4LhheweipW2kd1YB3LCJgAl12SJWi\nRz2dVsAnzllI65Nwwy33kK34FGe97RLq6YO8sifm+R/GhCJEyTrWTpCiIY3ppCkPW8ueOGJgMGbe\nXIuNI7RSWPoISoKCERhdxPqYovBorXDCAgFrv9IkecizkSYTJuWCK74INuTWtSM88MgoQVBgYLCH\ncqVEuRpQ7VPMqVQpC0nRG4zyqADIEvZNW5xtIIMKBRnSqgacdk8RScyxRwV8rDSISZeitaWGoSwd\nvaGnUzV5AGwCUrf55WjEe85TnL8m5ktf6GH5OYO8vw5yOmZGIAkUICyqW8STV4sFkSc9C1vEJopO\n1OQw36Q2Yy6tA9P8MsupVXsabaImtNuwfmQCn0HYJ3DOU8bRSTzlsMTkmMbaCqGyfL7Sz8xqmfCo\nACssHduDSTWbnjKsf3Q3/VVFUAlwBYEHbNfTMgmNxGEaBj8d0D97mMRZ7npoHQUNgTIIqwmkxciD\nhCOZOzXB47zCKp8nMyOQ1jMVtXl2d4sbq1BOHSZK6U47rDEY0yWyHm9TXNoFk9Ea84yPJISqzuLh\nCsHxFicUIrAYD62tGtP8A6NOH3rIoYBAeANWoEVepOlikFN0Dv5TBpTQ0iJ8F+tAKIGUHdIOBDSw\naR2RWXTYg7ESEYzjVBUvayDKaJ8hCxakRWQKLQ2pDpCUgJiAImApGEVXpSgn836CSpCqF6+25yrJ\nTKNlCSNCBAqZClYums09169iqGcv+ogKOxsxt/u78OwFCxMm5YWRmCNWXcXV60b5j788j/Gx41hc\nGeCnz13HUI8C5Vh5cp2v6TrRZIQtZFBQiFbCvjhhKjWIUGPThBvOOYm/uXMDNkkpVjT4IioQWFJ8\nmhAKMImlpAVCaZRSJM4SBgFzVlT5ddrLL3kvUfooZ68IsUohi1X0wTL32Q0J+DFmDVVYWO9HKENi\nI0gE/cODWGuYHB0lDEeYPx1Q7qsSaEknTlCugpcWYQO0gnIloKpDtEixHoxVCM6hkQRcf1EZFDgD\nu7ZJnu43zCkp5vUpDoQpJXWQriRy4Y/VoGKFLWq88pRrc6npMolv88LeHWxraDq+SMMmVESZdpaC\nhllakRjyJqSFKoL6e4s8+90AhaWEIqhaht7bw6GlAhQD/tNBasAbjYlL7BlLMCMR7lGDkQXsOoMx\nEpNleBlSq4XUZvXz+N3XcPnqT7HtmafopHtBWVJnUQR44bEuP8Z6pVDCIZzEe4EXFqkUqZ/mSw80\neOnLQ2AThLKUvCR2Gd55qljiIojUkUSWxBqGjq8y69wQQoE3gnZqaaMJLVhlCPwf2KLwFm/l+G6h\nQDgkAmnBWX/wHBngMYAEpREZ6KLIYZ3W44XFdCIoLqUrHAXZBS+QKqUgU5SbxLmZWBPigxi8wfow\nz2MwGc5ZvIGShZLyaDK8MxjpcmSd0vhAYU0JrKagAlJdRCtF6HO82i9/uoAl75bs2Rty6B+n9Pzi\nHITekqfveotwmiOWXcUFDzU49pT7mbNomOXDffzFzAV87BpNFI3yfr2IRGlUCYyPKXQDIObAaMJj\njR2Y1NNfr2IR9NcF9117Lmd8bh2TccJA4PMzKqBkRhuJVgJd1BiXF6t4S2NacOl18MlL9vML+1vW\n31gF1wIp0FVHOZAM6QJzBg3bt0pMDLdcUWdmHySpIokMiUronx3CGOzJPO2xhAEzxkC1h1mzy8ys\nhJTDXDyktcJbi/VRvrsdPBRYWeT16ZQ5w4q2dXQ7YDJDa2vMg1+ImD80yIxBz8BgyFRB4YqeaiAo\nGAuZoWsaaFFnTn0B403LS41R7h0xKK8pFCzz+qsEpoTZO8FAXRN6j7KCYBqEseBSXCjpnRvio4T+\nUh9KwWG1Mkp7ZgjFkYD3AuckpuN4w0MUJbT2x5gE2pFhMkpp77eUZxdJsgTxZIsrL/o2vY8spLzt\nWp7f/I3/q73zC5HrqgPw96PntHu0Z3CuddadMZkk27KxRMwuSVhDCbQPxrZKqiDUl1aqiKWiBSuk\nFrHik//64B+EioG2RvuiYqWotalQwdhsdLdpYnbbHe0Sd+OOMkvnKnf1Hj0+3BszE7PshtDcO3A+\nGO6Zc+/Dd+Z35zfnnJk5h6PzP8c6gzYGp/MNo4zDaZ3d92TDNYtGuWwfkF//5hjv+uQ7uHm8Rqyy\nH1Q1kgpqC1gLu1zCTG2eBM0OGxEZgxk2OKVY6cT5svIJ1miMhY4atP8+IJAqEqeBFFQ2oQiA0zit\nUE6RAk4rhgCXrkKscOkQrhKTqiXS7iymEuO6CSoFS8JQvjOPMTOkejNJokBtQTmwQwk66ZK6mNhp\ncAaVZht/KlJqsaOdgrEGlxgwFYyJwFSwRtFugXaaz39uO/eOVNldey+b9z/A0Sdu5+O37GWu9TTV\n2jxxtUF9936etRGPPJrwhtNf4NCDTUb37uCdY1BvTeOSGoefP8bO3fuoqjaj9QpDNuL41BQzs7Mc\nn52HqEGz0USnCd/4xTRfuWOcwwdvZ/LDT9O6vs1qlGK72XJdNRWxoxZhVNb+xDnSxKErMaZe4a3j\nr/H4R8+w4569DHXqLLmEuo3RCuKOxuouX7zvFj5z1zif/eCnUdEwSbpMHK9yarFLahNGmyuY6Q7P\nTS8ytZj1TJRZYvf2nVTVMM2aRUUdHGBHLRiNSqvZt0uVDu9LLLtmHZMK4lU4sxAz5ByxM+go4e8r\nmpFWysq1McaktLTGKoU1ith1aKg9pKrDT4+2mZqGhSVLFBlufjs06oZOYhhLDduiJnpxhXac0jZg\nl2GBGNdexl5foXUExiZqVG2NqnLUG3XskAatcPlrF29P2ZSu0F6ynK13MAtgmo6xtmJuoU2sHCrq\nstJeYGGmRjJmaW6ucK/+Hu2vTrLSms/+h+EqKJWQ0EWRZgu/ZEvXoDCsKkh1glGO56aneGb6CN+8\n534+8CGLTRXKOXQnZnFhlO/PHGV7F2rDFZJuiq44MNBOFMpl+03EgImzD4QNvxvX2wzmSiAifwX+\nAfytaJfL4DoG2x8Gvw2D7g+vbxua3vu3rHdRKZICgIgc38juNWVl0P1h8Nsw6P5QjjaUYpGVQCBQ\nHkJSCAQCfZQpKTxatMBlMuj+MPhtGHR/KEEbSjOnEAgEykGZegqBQKAEhKQQCAT6KDwpiMh7RGRO\nROZF5GDRPhtFRF4VkZdEZEZEjud1kYj8UkReyY/Voj17EZFDItIWkZM9dRd1zvcC/XoelxMiMlGc\n+f9cL+b/sIgs5nGYEZHbes49mPvPicj+YqzPIyKbRORXIvIHETklIp/K68sVA+99YQ/gKqAFbAOu\nBl4EbizS6RLcXwWuu6Duy8DBvHwQ+FLRnhf47QMmgJPrOZPtB/ozsi0DJ4EXSur/MPDARa69Mb+f\nrgG25vfZVQX7jwATedkCL+eepYpB0T2FPcC89/6P3vt/AU8CBwp2uhwOAI/l5ceAOwp0+T+8989D\nvpb4edZyPgA87jN+C7xJREaujOnFWcN/LQ4AT3rv/+m9/xPZhsd7Xje5DeC9P+u9/31ejoHTQIOS\nxaDopNAAzvQ8/3NeNwh44BkR+Z2IfCyvG/ben83LfwGGi1G7JNZyHqTYfCLvXh/qGbKV2l9EtgDj\nwAuULAZFJ4VB5ibv/QRwK3CfiOzrPemz/t9Afd87iM7At4FRYCdwFvhasTrrIyLXAj8E7vfed3vP\nlSEGRSeFRWBTz/O35XWlx3u/mB/bwI/JuqbL57p3+bFdnOGGWct5IGLjvV/23v/be/8f4DucHyKU\n0l9ENFlCOOy9/1FeXaoYFJ0UpoAbRGSriFwN3Ak8VbDTuojIG0XEnisD7wZOkrnfnV92N/CTYgwv\nibWcnwLuymfAJ4HXerq4peGCMfb7yeIAmf+dInKNiGwFbgCOXWm/XkREgO8Cp733j/ScKlcMipyN\n7ZlhfZlsdvihon026LyNbGb7ReDUOW/gzcAR4BXgWSAq2vUC7x+QdbFTsvHpR9ZyJpvx/lYel5eA\nXSX1fyL3O0H2Jhrpuf6h3H8OuLUE/jeRDQ1OADP547ayxSD8zDkQCPRR9PAhEAiUjJAUAoFAHyEp\nBAKBPkJSCAQCfYSkEAgE+ghJIRAI9BGSQiAQ6OO/S6nbH9f80BIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unloader = transforms.ToPILImage()  # reconvert into PIL image\n",
    "\n",
    "plt.ion()\n",
    "\n",
    "def imshow(tensor, title=None):\n",
    "    image = tensor.cpu().clone()  # we clone the tensor to not do changes on it\n",
    "    image = image.squeeze(0)      # remove the fake batch dimension\n",
    "    image = unloader(image)\n",
    "    plt.imshow(image)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001) # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "imshow(img[11], title='Image')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)\n",
      "  (fc): Linear(in_features=512, out_features=2, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "def set_parameter_requires_grad(model, feature_extract):\n",
    "    if feature_extract:\n",
    "        for param in model.parameters():\n",
    "            param.requires_grad = False\n",
    "            \n",
    "def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):\n",
    "    if model_name == \"resnet\":\n",
    "        model_ft = models.resnet18(pretrained=use_pretrained)\n",
    "        set_parameter_requires_grad(model_ft, feature_extract)\n",
    "        num_ftrs = model_ft.fc.in_features\n",
    "        model_ft.fc = nn.Linear(num_ftrs, num_classes)\n",
    "        input_size = 224\n",
    "    else:\n",
    "        print(\"model not implemented\")\n",
    "        return None, None\n",
    "        \n",
    "    return model_ft, input_size\n",
    "\n",
    "model_ft, input_size = initialize_model(model_name, \n",
    "                    num_classes, feature_extract, use_pretrained=True)\n",
    "print(model_ft)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ft.layer1[0].conv1.weight.requires_grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ft.fc.weight.requires_grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_model(model, dataloaders, loss_fn, optimizer, num_epochs=5):\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.\n",
    "    val_acc_history = []\n",
    "    for epoch in range(num_epochs):\n",
    "        for phase in [\"train\", \"val\"]:\n",
    "            running_loss = 0.\n",
    "            running_corrects = 0.\n",
    "            if phase == \"train\":\n",
    "                model.train()\n",
    "            else:\n",
    "                model.eval()\n",
    "                \n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs, labels = inputs.to(device), labels.to(device)\n",
    "                \n",
    "                with torch.autograd.set_grad_enabled(phase==\"train\"):\n",
    "                    outputs = model(inputs) # bsize * 2\n",
    "                    loss = loss_fn(outputs, labels) \n",
    "                    \n",
    "                preds = outputs.argmax(dim=1)\n",
    "                if phase == \"train\":\n",
    "                    optimizer.zero_grad()\n",
    "                    loss.backward()\n",
    "                    optimizer.step()\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds.view(-1) == labels.view(-1)).item()\n",
    "                \n",
    "            epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
    "            epoch_acc = running_corrects / len(dataloaders[phase].dataset)\n",
    "            \n",
    "            print(\"Phase {} loss: {}, acc: {}\".format(phase, epoch_loss, epoch_acc))\n",
    "            \n",
    "            if phase == \"val\" and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "            if phase == \"val\":\n",
    "                val_acc_history.append(epoch_acc)\n",
    "    model.load_state_dict(best_model_wts)    \n",
    "    return model, val_acc_history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ft = model_ft.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, \n",
    "                                   model_ft.parameters()), lr=0.001, momentum=0.9)\n",
    "loss_fn = nn.CrossEntropyLoss()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phase train loss: 0.2441009590860273, acc: 0.9139344262295082\n",
      "Phase val loss: 0.2058036023495244, acc: 0.9477124183006536\n",
      "Phase train loss: 0.2242034280397853, acc: 0.9221311475409836\n",
      "Phase val loss: 0.19933121480972937, acc: 0.9477124183006536\n",
      "Phase train loss: 0.2179500304284643, acc: 0.930327868852459\n",
      "Phase val loss: 0.19292019200480842, acc: 0.9477124183006536\n",
      "Phase train loss: 0.2038783010889272, acc: 0.9221311475409836\n",
      "Phase val loss: 0.2022019473750607, acc: 0.9281045751633987\n",
      "Phase train loss: 0.20605210031642288, acc: 0.9180327868852459\n",
      "Phase val loss: 0.18852663916700027, acc: 0.9477124183006536\n",
      "Phase train loss: 0.1799576844348282, acc: 0.9426229508196722\n",
      "Phase val loss: 0.18889451397010704, acc: 0.9477124183006536\n",
      "Phase train loss: 0.16676783659419075, acc: 0.9426229508196722\n",
      "Phase val loss: 0.1854035053280444, acc: 0.9477124183006536\n",
      "Phase train loss: 0.20258395642530722, acc: 0.930327868852459\n",
      "Phase val loss: 0.1881853450162738, acc: 0.934640522875817\n",
      "Phase train loss: 0.17906492948532104, acc: 0.9180327868852459\n",
      "Phase val loss: 0.17941297795258315, acc: 0.954248366013072\n",
      "Phase train loss: 0.15364321333463074, acc: 0.9631147540983607\n",
      "Phase val loss: 0.18958801722604465, acc: 0.9281045751633987\n",
      "Phase train loss: 0.19896865452899307, acc: 0.9139344262295082\n",
      "Phase val loss: 0.1826314626176373, acc: 0.954248366013072\n",
      "Phase train loss: 0.16911793878821077, acc: 0.9344262295081968\n",
      "Phase val loss: 0.18108942452209448, acc: 0.9477124183006536\n",
      "Phase train loss: 0.16306845306373033, acc: 0.9467213114754098\n",
      "Phase val loss: 0.1891336505806524, acc: 0.9281045751633987\n",
      "Phase train loss: 0.1875694076545903, acc: 0.9385245901639344\n",
      "Phase val loss: 0.1793875343659345, acc: 0.9477124183006536\n",
      "Phase train loss: 0.20147151096922453, acc: 0.9139344262295082\n",
      "Phase val loss: 0.18119409422274507, acc: 0.9411764705882353\n"
     ]
    }
   ],
   "source": [
    "_, ohist = train_model(model_ft, dataloaders_dict, loss_fn, optimizer, num_epochs=num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phase train loss: 0.73858080437926, acc: 0.47950819672131145\n",
      "Phase val loss: 0.704963364632301, acc: 0.46405228758169936\n",
      "Phase train loss: 0.668987612255284, acc: 0.5614754098360656\n",
      "Phase val loss: 0.6597700851415497, acc: 0.6339869281045751\n",
      "Phase train loss: 0.6411691278707786, acc: 0.6557377049180327\n",
      "Phase val loss: 0.6726375681902069, acc: 0.5751633986928104\n",
      "Phase train loss: 0.6194371883986426, acc: 0.6352459016393442\n",
      "Phase val loss: 0.6313814318257999, acc: 0.6274509803921569\n",
      "Phase train loss: 0.6170555851498588, acc: 0.6475409836065574\n",
      "Phase val loss: 0.6528662945709977, acc: 0.6274509803921569\n",
      "Phase train loss: 0.6110719637792619, acc: 0.6762295081967213\n",
      "Phase val loss: 0.626404657472972, acc: 0.6405228758169934\n",
      "Phase train loss: 0.5864127718034338, acc: 0.639344262295082\n",
      "Phase val loss: 0.6282203664966658, acc: 0.6405228758169934\n",
      "Phase train loss: 0.5998562847981688, acc: 0.6680327868852459\n",
      "Phase val loss: 0.6236733716297773, acc: 0.6274509803921569\n",
      "Phase train loss: 0.5662176755608105, acc: 0.6885245901639344\n",
      "Phase val loss: 0.5790788285872516, acc: 0.6862745098039216\n",
      "Phase train loss: 0.5466401464626437, acc: 0.7131147540983607\n",
      "Phase val loss: 0.5834652006236556, acc: 0.7124183006535948\n",
      "Phase train loss: 0.5393341779708862, acc: 0.7295081967213115\n",
      "Phase val loss: 0.5651591182534211, acc: 0.6797385620915033\n",
      "Phase train loss: 0.5473490689621597, acc: 0.7172131147540983\n",
      "Phase val loss: 0.5568503246587866, acc: 0.673202614379085\n",
      "Phase train loss: 0.5429437048122531, acc: 0.7090163934426229\n",
      "Phase val loss: 0.6801646998505188, acc: 0.6339869281045751\n",
      "Phase train loss: 0.512938850238675, acc: 0.7254098360655737\n",
      "Phase val loss: 0.6064363223275328, acc: 0.6862745098039216\n",
      "Phase train loss: 0.5331279508403091, acc: 0.6885245901639344\n",
      "Phase val loss: 0.5726334435098311, acc: 0.6928104575163399\n"
     ]
    }
   ],
   "source": [
    "model_scratch, _ = initialize_model(model_name, \n",
    "                    num_classes, feature_extract=False, use_pretrained=False)\n",
    "model_scratch = model_scratch.to(device)\n",
    "optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, \n",
    "                                   model_scratch.parameters()), lr=0.001, momentum=0.9)\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "_, scratch_hist = train_model(model_scratch, dataloaders_dict, loss_fn, optimizer, num_epochs=num_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们来plot模型训练时候loss的变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Sk6HPqe6vYKWrORRvct9Tu+7xjm6vEU3z0ZvAIcCHwLbQ643dZXbIXlNTUIWn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\nhFJBvF2GujYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot the training curves of validation accuracy vs. number \n",
    "#  of training epochs for the transfer learning method and\n",
    "#  the model trained from scratch\n",
    "\n",
    "plt.title(\"Validation Accuracy vs. Number of Training Epochs\")\n",
    "plt.xlabel(\"Training Epochs\")\n",
    "plt.ylabel(\"Validation Accuracy\")\n",
    "plt.plot(range(1,num_epochs+1),ohist,label=\"Pretrained\")\n",
    "plt.plot(range(1,num_epochs+1),scratch_hist,label=\"Scratch\")\n",
    "plt.ylim((0,1.))\n",
    "plt.xticks(np.arange(1, num_epochs+1, 1.0))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 课后学习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [BERT](https://github.com/huggingface/pytorch-pretrained-BERT)\n",
    "- [ElMo](https://github.com/allenai/allennlp/blob/master/allennlp/modules/elmo.py)\n",
    "- [Torch Vision Models](https://github.com/pytorch/vision/tree/master/torchvision/models)"
   ]
  }
 ],
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